sha
stringlengths 40
40
| text
stringlengths 0
13.4M
| id
stringlengths 2
117
| tags
list | created_at
stringlengths 25
25
| metadata
stringlengths 2
31.7M
| last_modified
stringlengths 25
25
|
|---|---|---|---|---|---|---|
850d8a8d95548a0ca04fdd5faa158bfdcf6ff797
|
```bib
@inproceedings{novikova-etal-2018-rankme,
title = "RankME: Reliable Human Ratings for Natural Language Generation",
author = "Novikova, Jekaterina and
Duvsek, Ondvrej and
Rieser, Verena",
booktitle = "Proceedings of the NAACL2018",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2012",
doi = "10.18653/v1/N18-2012",
pages = "72--78",
}
```
|
metaeval/rankme-nlg-acceptability
|
[
"task_categories:text-classification",
"task_ids:acceptability-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-02-01T14:16:13+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "task_ids": ["acceptability-classification"]}
|
2023-02-01T14:27:06+00:00
|
4476049b677e163e55a61797af80a325aeacba89
|
# Dataset Card for "nowiki_abstract_urls_20230120"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jkorsvik/nowiki_abstract_urls_20230120
|
[
"region:us"
] |
2023-02-01T14:16:29+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 126004455, "num_examples": 605457}], "download_size": 66525868, "dataset_size": 126004455}}
|
2023-02-01T14:20:35+00:00
|
69fc984e8131bb5540d20444e11162f027bbd1f4
|
# Dataset Card for "nq"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://ai.google.com/research/NaturalQuestions](https://ai.google.com/research/NaturalQuestions)
### Dataset Summary
This is a modified version of the original Natural Questions (nq) dataset for retrieval tasks. The original is availabe [here](https://ai.google.com/research/NaturalQuestions).
It contains google queries and an entire stripped wikipedia article for each query.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```json
{
"question": "who competes in miss universe miss america or miss usa",
"context": "Miss USA - Wikipedia\nThe Miss USA is an American beauty pageant that has been held annually since 1952 to select the Amer ...",
}
```
### Data Fields
The data fields are the same among all splits.
- `question`: a `string` feature.
- `context`: a `string` feature.
## Additional Information
### Licensing Information
This dataset is distributed under the cc-by-sa-3.0 license.
|
LLukas22/nq
|
[
"task_categories:sentence-similarity",
"task_categories:feature-extraction",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] |
2023-02-01T14:18:37+00:00
|
{"language": ["en"], "license": "cc-by-sa-3.0", "task_categories": ["sentence-similarity", "feature-extraction"]}
|
2023-04-30T19:07:17+00:00
|
61e8e86dac364d1bfaf38d663415cbba76e8e4f8
|
# Dataset Card for "scidocs"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://github.com/allenai/scidocs](https://github.com/allenai/scidocs)
### Dataset Summary
This is a modified version of the original scidocs dataset for retrieval tasks. The original is availabe [here](https://github.com/allenai/scidocs).
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```json
{
"title": "Discovery of inference rules for question-answering",
"abstract": "One of the main challenges in question-answering is the potential mismatch between the expressions in questions and ...",
}
```
### Data Fields
The data fields are the same among all splits.
- `title`: a `string` feature.
- `abstract`: a `string` feature.
## Additional Information
### Licensing Information
This dataset is distributed under the cc-by-4.0 license.
### Citation Information
BibTeX:
```json
@inproceedings{specter2020cohan,
title={SPECTER: Document-level Representation Learning using Citation-informed Transformers},
author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
booktitle={ACL},
year={2020}
}
```
|
LLukas22/scidocs
|
[
"task_categories:sentence-similarity",
"task_categories:feature-extraction",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
2023-02-01T14:19:31+00:00
|
{"language": ["en"], "license": "cc-by-4.0", "task_categories": ["sentence-similarity", "feature-extraction"]}
|
2023-04-30T18:45:23+00:00
|
244a63f9a0a05cc80daaa10bfcea5c8810fb7666
|
# Dataset Card for "OxfordFlowers_test_facebook_opt_2.7b_Attributes_Caption_ns_6149"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordFlowers_test_facebook_opt_2.7b_Attributes_Caption_ns_6149
|
[
"region:us"
] |
2023-02-01T14:46:32+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 269129188.375, "num_examples": 6149}, {"name": "fewshot_0_bs_16", "num_bytes": 267298050.375, "num_examples": 6149}, {"name": "fewshot_3_bs_16", "num_bytes": 272760392.375, "num_examples": 6149}], "download_size": 796875873, "dataset_size": 809187631.125}}
|
2023-02-01T17:31:19+00:00
|
b1600b08af83ce6ac15f9348080848ab8df35e2b
|
# Dataset Card for "ahbot_wakeword"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AhBotNLP/ahbot_wakeword
|
[
"task_categories:audio-classification",
"region:us"
] |
2023-02-01T15:01:45+00:00
|
{"task_categories": ["audio-classification"], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ahbot", "1": "ahbot_close", "2": "background_noise"}}}}], "splits": [{"name": "train", "num_bytes": 1190845036.86, "num_examples": 1124}], "download_size": 0, "dataset_size": 1190845036.86}}
|
2023-03-05T16:06:32+00:00
|
a4b6db331a366bb34c8bd61bdfff3d4fd04023c1
|
N/A. (2021). Yahoo! Answers (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5259952
|
breadlicker45/1m-YA-dataset
|
[
"region:us"
] |
2023-02-01T15:42:08+00:00
|
{}
|
2023-02-04T13:53:28+00:00
|
10ba1f7bd12132c1e7cd7226d8c4ae37b4b910fd
|
# Dataset Card for "OxfordFlowers_test_facebook_opt_2.7b_Visclues_ns_6149"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordFlowers_test_facebook_opt_2.7b_Visclues_ns_6149
|
[
"region:us"
] |
2023-02-01T15:43:01+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 267858097.375, "num_examples": 6149}, {"name": "fewshot_1_bs_16", "num_bytes": 270237106.375, "num_examples": 6149}, {"name": "fewshot_3_bs_16", "num_bytes": 274972317.375, "num_examples": 6149}], "download_size": 797641513, "dataset_size": 813067521.125}}
|
2023-02-01T18:51:09+00:00
|
93cfe0582f4a190dba43be42f9512b572444e8ab
|
# Dataset Card for "mogumogu_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
apapa/mogumogu_dataset
|
[
"region:us"
] |
2023-02-01T16:10:02+00:00
|
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text (string)", "dtype": "string"}, {"name": "phonetic_detail (json)", "dtype": "string"}, {"name": "word_detail (json)", "dtype": "string"}, {"name": "dialect_region (string)", "dtype": "string"}, {"name": "sentence_type (string)", "dtype": "string"}, {"name": "speaker_id (string)", "dtype": "string"}, {"name": "id (string)", "dtype": "string"}, {"name": "Unnamed: 8", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 419112689.8, "num_examples": 4270}, {"name": "test", "num_bytes": 168967037.04, "num_examples": 1680}], "download_size": 531996662, "dataset_size": 588079726.84}}
|
2023-02-01T16:11:07+00:00
|
8aa9eb882aba938b1a972331072ea683f664aef0
|
# Overview
This repository contains the dataset to the paper ["Causal Reasoning of Entities and Events in Procedural Texts" and the dataset **C**ausal **R**easoning of **E**ntities and **E**vents in **P**rocedural Texts (CREPE)](https://arxiv.org/pdf/2301.10896v2.pdf).
# Files
- `data_dev_v2.json` is the development set of CREPE.
- `data_test_v2.json` is the test set of CREPE.
---
# Explanations of the CREPE dataset
There are 6 columns in the dataset, namely `goal`, `steps`, `event`, `event_answer`, `entity`, `entity_answer`.
- `goal` denotes the goal of a procedure.
- `steps` is a list containing all steps involved in a procedure.
- `event` is an event whose likelihood of happening change due to the events in steps.
- `event_answer` is the ground truth likelihood change. See below for glossary.
- `entity` is the entity that directly relates to the `event`. Its state change will have a direct impact on the likelihood of the `event`
- `entity_answer` is the ground truth entity state change. See below for glossary.
# Glossary
|Label|Literal Mearning|
|---|---|
| 0 | `event`/`entity state` is _less likely_ to happen comparing to the previous step. |
| 1 | `event`/`entity state` is _equally likely_ to happen comparing to the previous step. |
| 2 | `event`/`entity state` is _more likely_ to happen comparing to the previous step. |
---
# Demonstration
`goal`: Clean up kitchen counter
`steps`:
1. Wear rubber gloves.
2. Get towels and wipes.
3. Use wipes to wipe kitchen counter.
4. Use towels to clean kitchen counter.
5. Store the gloves.
`event`: The likelihood that "_My skin makes contact with things I touch_" after the execution of each step.
`ground truth answers`:
|Step Number|Label|Literal Mearning|
|--|------|-------|
|1|0 | "less likely" |
|2|1 | "equally likely" |
|3|1 | "equally likely" |
|4|1 | "equally likely" |
|5|2 | "more likely" |
`entity state`: _hands_ are _covered_
`ground truth answers`:
|Step Number|Label|Literal Mearning|
|--|------|-------|
|5|2 | "more likely" |
|2|1 | "equally likely" |
|3|1 | "equally likely" |
|4|1 | "equally likely" |
|1|0 | "less likely" |
|
zharry29/CREPE
|
[
"language:en",
"license:cc-by-4.0",
"arxiv:2301.10896",
"region:us"
] |
2023-02-01T16:36:04+00:00
|
{"language": ["en"], "license": "cc-by-4.0"}
|
2023-02-01T21:33:44+00:00
|
f68cb893ee34e42e6c9c8156d803f3abdc4a6b39
|
# Dataset Card for "denoising-dirty-documents-cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joytafty/denoising-dirty-documents-cleaned
|
[
"region:us"
] |
2023-02-01T17:00:08+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 6620518.0, "num_examples": 144}], "download_size": 0, "dataset_size": 6620518.0}}
|
2023-02-01T22:34:01+00:00
|
7502c330be750b55b6a0b7e11bcfb30b55c70db1
|
# Dataset Card for "denoising-dirty-documents-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joytafty/denoising-dirty-documents-train
|
[
"region:us"
] |
2023-02-01T17:00:16+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 19395270.0, "num_examples": 144}], "download_size": 0, "dataset_size": 19395270.0}}
|
2023-02-03T20:01:57+00:00
|
f40b8a5a3907de7f798a465a4b80018ae4501433
|
# Dataset Card for "serviall_multiclass"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bggmyfuture-ai/serviall_multiclass
|
[
"region:us"
] |
2023-02-01T17:47:12+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "SubFamilia", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1735553, "num_examples": 17643}], "download_size": 747548, "dataset_size": 1735553}}
|
2023-02-01T17:47:16+00:00
|
d191d53410ce6f74939699774250f8f9760bbebf
|
# Dataset Card for "relbert/nell_relation_similarity"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/)
- **Dataset:** Relational similarity dataset based on the NELL-one
### Dataset Summary
[NELL-one](https://huggingface.co/datasets/relbert/nell) cleaned dataset compiled for relational similarity.
## Dataset Structure
### Data Instances
An example of `test` looks as follows.
```shell
{
"relation_type": "concept:automobilemakerdealersincity",
"positives": [["Lexus", "Dallas"], ["Buick", "Columbus"], ...,
"negatives": []}
}
```
### Data Splits
| train |validation| test|
|--------:|---------:|---------:|
| 30| 3 | 5 |
### Citation Information
```
@inproceedings{xiong-etal-2018-one,
title = "One-Shot Relational Learning for Knowledge Graphs",
author = "Xiong, Wenhan and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1223",
doi = "10.18653/v1/D18-1223",
pages = "1980--1990",
abstract = "Knowledge graphs (KG) are the key components of various natural language processing applications. To further expand KGs{'} coverage, previous studies on knowledge graph completion usually require a large number of positive examples for each relation. However, we observe long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.",
}
```
|
relbert/nell_relational_similarity
|
[
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"license:other",
"region:us"
] |
2023-02-01T18:24:45+00:00
|
{"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "pretty_name": "Relational similarity dataset based on the NELL-one"}
|
2023-03-10T11:18:11+00:00
|
af32591e78a48e31eedb8e674c2524385f5dd96c
|
# Dataset Card for "denoising-dirty-documents-trained_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joytafty/denoising-dirty-documents-trained_cleaned
|
[
"region:us"
] |
2023-02-01T20:31:00+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 6620518.0, "num_examples": 144}], "download_size": 0, "dataset_size": 6620518.0}}
|
2023-02-03T20:01:54+00:00
|
02f3b3da58943687b4eee8e5b10654d83c4b114c
|
# Dataset Card for "denoising-dirty-documents-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joytafty/denoising-dirty-documents-test
|
[
"region:us"
] |
2023-02-01T20:31:15+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 9838202.0, "num_examples": 72}], "download_size": 0, "dataset_size": 9838202.0}}
|
2023-02-03T20:02:00+00:00
|
a961ddd735bf43b1b406b5a64f88bc2d185ec59b
|
## DataComp Pools
This repository contains metadata files for DataComp. For details on how to use the metadata, please visit [our website](https://www.datacomp.ai/) and our [github repository](https://github.com/mlfoundations/datacomp).
We distribute the image url-text samples and metadata under a standard Creative Common CC-BY-4.0 license. The individual images are under their own copyrights.
## Terms and Conditions
We have terms of service that are similar to those adopted by HuggingFace (https://huggingface.co/terms-of-service), which covers their dataset library. Specifically, any content you download, access or use from our index, is at your own risk and subject to the terms of service or copyright limitations accompanying such content. The image url-text index, which is a research artifact, is provided as is. By using said index, you assume all risks, including but not limited to, liabilities related to image downloading and storage.
|
mlfoundations/datacomp_pools
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-02-01T20:36:30+00:00
|
{"license": "cc-by-4.0"}
|
2023-08-21T20:43:57+00:00
|
279e3fe33ed35c0a91947dad38570e70fa9819e4
|
johnbakerjr/world_data_viz
|
[
"license:mit",
"region:us"
] |
2023-02-01T20:59:56+00:00
|
{"license": "mit"}
|
2023-02-01T21:00:28+00:00
|
|
056543867547d96ba3056f8adb0dc30a224ceff5
|
# Dataset Card for "patched_test_p_40_m1_predictions_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roa7n/patched_test_p_40_m1_predictions_v2
|
[
"region:us"
] |
2023-02-01T21:10:13+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1471779018, "num_examples": 2637494}], "download_size": 127557931, "dataset_size": 1471779018}}
|
2023-02-01T21:10:32+00:00
|
bb128f33d65d830c5b861f05cf9841fcd4645e76
|
# Dataset Card for "OxfordFlowers_test_facebook_opt_6.7b_Visclues_ns_6149"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordFlowers_test_facebook_opt_6.7b_Visclues_ns_6149
|
[
"region:us"
] |
2023-02-01T21:29:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 267860040.375, "num_examples": 6149}, {"name": "fewshot_1_bs_16", "num_bytes": 270237117.375, "num_examples": 6149}, {"name": "fewshot_3_bs_16", "num_bytes": 274972348.375, "num_examples": 6149}], "download_size": 785684330, "dataset_size": 813069506.125}}
|
2023-02-02T02:51:33+00:00
|
04d6d21f66e9f812718c58856852874506a323ff
|
# Dataset Card for "dianauribelarge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
juancopi81/dianauribelarge
|
[
"task_categories:automatic-speech-recognition",
"whisper",
"whispering",
"large",
"region:us"
] |
2023-02-01T21:50:57+00:00
|
{"task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "CHANNEL_NAME", "dtype": "string"}, {"name": "URL", "dtype": "string"}, {"name": "TITLE", "dtype": "string"}, {"name": "DESCRIPTION", "dtype": "string"}, {"name": "TRANSCRIPTION", "dtype": "string"}, {"name": "SEGMENTS", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24130463, "num_examples": 371}], "download_size": 11409735, "dataset_size": 24130463}, "tags": ["whisper", "whispering", "large"]}
|
2023-02-07T14:01:41+00:00
|
1f984e50c38b7ffb5a853b2135732fbf6ac4b636
|
# Dataset Card for "ts_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ocolegro/ts_train
|
[
"region:us"
] |
2023-02-01T22:20:12+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23751416, "num_examples": 11414}], "download_size": 8011655, "dataset_size": 23751416}}
|
2023-02-01T22:20:16+00:00
|
c15a3986213714cadd85575e208edcd1c58684cf
|
This data comes from "JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset" by Ruth-Ann Armstrong, John Hewitt, Christopher Manning. Please cite the original work if you make use of this data:
```
@article{DBLP:journals/corr/abs-2212-03419,
author = {Ruth{-}Ann Armstrong and
John Hewitt and
Christopher D. Manning},
title = {JamPatoisNLI: {A} Jamaican Patois Natural Language Inference Dataset},
journal = {CoRR},
volume = {abs/2212.03419},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2212.03419},
doi = {10.48550/arXiv.2212.03419},
eprinttype = {arXiv},
eprint = {2212.03419},
timestamp = {Mon, 02 Jan 2023 15:09:55 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2212-03419.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
WillHeld/JamPatoisNLI
|
[
"arxiv:2212.03419",
"region:us"
] |
2023-02-01T22:56:34+00:00
|
{"dataset_info": {"features": [{"name": "Number", "dtype": "int64"}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}], "splits": [{"name": "train", "num_bytes": 32336, "num_examples": 250}, {"name": "val", "num_bytes": 27515, "num_examples": 200}, {"name": "test", "num_bytes": 27342, "num_examples": 200}], "download_size": 67207, "dataset_size": 87193}}
|
2023-02-01T23:11:13+00:00
|
c0e3a12cd19773437ccee37764cbacaeadadad6e
|
Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
This version is augmented with context retrieved from the textbooks provided with the original dataset using cosine similarity.
<h4>Citation information:</h4>
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
|
GBaker/MedQA-USMLE-4-options-hf-cosine-similarity
|
[
"license:cc-by-sa-4.0",
"region:us"
] |
2023-02-01T23:13:18+00:00
|
{"license": "cc-by-sa-4.0"}
|
2023-02-02T18:57:53+00:00
|
4a7edff56500a97b8733d5c945c0c2acdba8d4c8
|
# FBI Cap Meme LoRA
# Use Cases
The LoRA is in itself very compatible with the most diverse model. However, it is most effective when used with Kenshi or AbyssOrangeMix2.
The LoRA itself was trained with the token: ```skistyle```.
You most likely want to add ```fbi cap, fbi``` to force the cap.
The models mentioned right now
1. AbyssOrangeMix2 from [WarriorMama777](https://huggingface.co/WarriorMama777/OrangeMixs)
2. Kenshi Model from [Luna](https://huggingface.co/SweetLuna/Kenshi)
## Strength
I would personally use these strength with the assosiated model:
- 0.75-0.85 for AbyssOrangeMix2
- 0.65-0.85 for Kenshi
# Showcase
**Example 1**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/FBI-meme_LoRA/resolve/main/preview/Preview%20(1).png"/>
```
skistyle, fbi cap, cap,
a girl, short white hair, grey eyes, masterpiece, highest quality
Steps: 32, Sampler: Euler a, CFG scale: 7
```
**Example 2**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/FBI-meme_LoRA/resolve/main/preview/Preview%20(2).png"/>
```
skistyle, fbi cap, cap,
1girl, solo, hat, weapon, sunglasses, gun, baseball cap, braid, red hair, long hair, looking at viewer, spot color, white background, simple background, gloves, jacket, upper body, single braid
Steps: 32, Sampler: Euler a, CFG scale: 7
```
**Example 3**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/FBI-meme_LoRA/resolve/main/preview/Preview%20(3).png"/>
```
skistyle, fbi cap, fbi,
1girl, solo, highly detailed, masterpiece
Steps: 32, Sampler: Euler a, CFG scale: 7
```
# License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
Nerfgun3/FBI-meme_LoRA
|
[
"language:en",
"license:creativeml-openrail-m",
"stable-diffusion",
"text-to-image",
"image-to-image",
"region:us"
] |
2023-02-01T23:31:36+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "thumbnail": "https://huggingface.co/datasets/Nerfgun3/FBI-meme_LoRA/resolve/main/preview/Preview%20(4).png", "tags": ["stable-diffusion", "text-to-image", "image-to-image"], "inference": false}
|
2023-02-01T23:38:31+00:00
|
3b5949d968d1fbc3facce39769ba00aa13404ffc
|
# MMLU dataset
Measuring Massive Multitask Language Understanding: https://github.com/hendrycks/test
```
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
```
|
lukaemon/mmlu
|
[
"region:us"
] |
2023-02-02T00:42:27+00:00
|
{"dataset_info": [{"config_name": "high_school_european_history", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 268045, "num_examples": 164}, {"name": "validation", "num_bytes": 27437, "num_examples": 17}, {"name": "train", "num_bytes": 9449, "num_examples": 4}], "download_size": 166184960, "dataset_size": 304931}, {"config_name": "business_ethics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 32080, "num_examples": 99}, {"name": "validation", "num_bytes": 2735, "num_examples": 10}, {"name": "train", "num_bytes": 1770, "num_examples": 4}], "download_size": 166184960, "dataset_size": 36585}, {"config_name": "clinical_knowledge", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 60710, "num_examples": 264}, {"name": "validation", "num_bytes": 6231, "num_examples": 28}, {"name": "train", "num_bytes": 1026, "num_examples": 4}], "download_size": 166184960, "dataset_size": 67967}, {"config_name": "medical_genetics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 20021, "num_examples": 99}, {"name": "validation", "num_bytes": 2590, "num_examples": 10}, {"name": "train", "num_bytes": 854, "num_examples": 4}], "download_size": 166184960, "dataset_size": 23465}, {"config_name": "high_school_us_history", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 294113, "num_examples": 203}, {"name": "validation", "num_bytes": 30202, "num_examples": 21}, {"name": "train", "num_bytes": 7341, "num_examples": 4}], "download_size": 166184960, "dataset_size": 331656}, {"config_name": "high_school_physics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 58279, "num_examples": 150}, {"name": "validation", "num_bytes": 6189, "num_examples": 16}, {"name": "train", "num_bytes": 1193, "num_examples": 4}], "download_size": 166184960, "dataset_size": 65661}, {"config_name": "high_school_world_history", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 376057, "num_examples": 236}, {"name": "validation", "num_bytes": 44247, "num_examples": 25}, {"name": "train", "num_bytes": 4339, "num_examples": 4}], "download_size": 166184960, "dataset_size": 424643}, {"config_name": "virology", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 37496, "num_examples": 165}, {"name": "validation", "num_bytes": 5124, "num_examples": 17}, {"name": "train", "num_bytes": 848, "num_examples": 4}], "download_size": 166184960, "dataset_size": 43468}, {"config_name": "high_school_microeconomics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 73766, "num_examples": 237}, {"name": "validation", "num_bytes": 7165, "num_examples": 25}, {"name": "train", "num_bytes": 855, "num_examples": 4}], "download_size": 166184960, "dataset_size": 81786}, {"config_name": "econometrics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 45258, "num_examples": 113}, {"name": "validation", "num_bytes": 4552, "num_examples": 11}, {"name": "train", "num_bytes": 1452, "num_examples": 4}], "download_size": 166184960, "dataset_size": 51262}, {"config_name": "college_computer_science", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 41567, "num_examples": 99}, {"name": "validation", "num_bytes": 4157, "num_examples": 10}, {"name": "train", "num_bytes": 2496, "num_examples": 4}], "download_size": 166184960, "dataset_size": 48220}, {"config_name": "high_school_biology", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 107194, "num_examples": 309}, {"name": "validation", "num_bytes": 10054, "num_examples": 31}, {"name": "train", "num_bytes": 1481, "num_examples": 4}], "download_size": 166184960, "dataset_size": 118729}, {"config_name": "abstract_algebra", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 18504, "num_examples": 99}, {"name": "validation", "num_bytes": 1852, "num_examples": 10}, {"name": "train", "num_bytes": 698, "num_examples": 4}], "download_size": 166184960, "dataset_size": 21054}, {"config_name": "professional_accounting", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 122155, "num_examples": 281}, {"name": "validation", "num_bytes": 13749, "num_examples": 30}, {"name": "train", "num_bytes": 1683, "num_examples": 4}], "download_size": 166184960, "dataset_size": 137587}, {"config_name": "philosophy", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 77720, "num_examples": 310}, {"name": "validation", "num_bytes": 8352, "num_examples": 33}, {"name": "train", "num_bytes": 698, "num_examples": 4}], "download_size": 166184960, "dataset_size": 86770}, {"config_name": "professional_medicine", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 214495, "num_examples": 271}, {"name": "validation", "num_bytes": 23003, "num_examples": 30}, {"name": "train", "num_bytes": 2531, "num_examples": 4}], "download_size": 166184960, "dataset_size": 240029}, {"config_name": "nutrition", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 90097, "num_examples": 305}, {"name": "validation", "num_bytes": 7826, "num_examples": 32}, {"name": "train", "num_bytes": 1455, "num_examples": 4}], "download_size": 166184960, "dataset_size": 99378}, {"config_name": "global_facts", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 17571, "num_examples": 99}, {"name": "validation", "num_bytes": 1646, "num_examples": 9}, {"name": "train", "num_bytes": 666, "num_examples": 4}], "download_size": 166184960, "dataset_size": 19883}, {"config_name": "machine_learning", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 32810, "num_examples": 111}, {"name": "validation", "num_bytes": 2701, "num_examples": 10}, {"name": "train", "num_bytes": 1971, "num_examples": 4}], "download_size": 166184960, "dataset_size": 37482}, {"config_name": "security_studies", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 202888, "num_examples": 244}, {"name": "validation", "num_bytes": 21703, "num_examples": 26}, {"name": "train", "num_bytes": 3889, "num_examples": 4}], "download_size": 166184960, "dataset_size": 228480}, {"config_name": "public_relations", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 27797, "num_examples": 109}, {"name": "validation", "num_bytes": 4180, "num_examples": 11}, {"name": "train", "num_bytes": 1064, "num_examples": 4}], "download_size": 166184960, "dataset_size": 33041}, {"config_name": "professional_psychology", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 221179, "num_examples": 611}, {"name": "validation", "num_bytes": 28283, "num_examples": 68}, {"name": "train", "num_bytes": 1348, "num_examples": 4}], "download_size": 166184960, "dataset_size": 250810}, {"config_name": "prehistory", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 87125, "num_examples": 323}, {"name": "validation", "num_bytes": 9915, "num_examples": 34}, {"name": "train", "num_bytes": 1484, "num_examples": 4}], "download_size": 166184960, "dataset_size": 98524}, {"config_name": "anatomy", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 31810, "num_examples": 134}, {"name": "validation", "num_bytes": 2879, "num_examples": 13}, {"name": "train", "num_bytes": 717, "num_examples": 4}], "download_size": 166184960, "dataset_size": 35406}, {"config_name": "human_sexuality", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 31019, "num_examples": 130}, {"name": "validation", "num_bytes": 2042, "num_examples": 11}, {"name": "train", "num_bytes": 861, "num_examples": 4}], "download_size": 166184960, "dataset_size": 33922}, {"config_name": "college_medicine", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 80363, "num_examples": 172}, {"name": "validation", "num_bytes": 7079, "num_examples": 21}, {"name": "train", "num_bytes": 1434, "num_examples": 4}], "download_size": 166184960, "dataset_size": 88876}, {"config_name": "high_school_government_and_politics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 64098, "num_examples": 192}, {"name": "validation", "num_bytes": 6317, "num_examples": 20}, {"name": "train", "num_bytes": 1314, "num_examples": 4}], "download_size": 166184960, "dataset_size": 71729}, {"config_name": "college_chemistry", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 23837, "num_examples": 99}, {"name": "validation", "num_bytes": 2039, "num_examples": 7}, {"name": "train", "num_bytes": 892, "num_examples": 4}], "download_size": 166184960, "dataset_size": 26768}, {"config_name": "logical_fallacies", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 48758, "num_examples": 162}, {"name": "validation", "num_bytes": 4699, "num_examples": 17}, {"name": "train", "num_bytes": 1256, "num_examples": 4}], "download_size": 166184960, "dataset_size": 54713}, {"config_name": "high_school_geography", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 40424, "num_examples": 197}, {"name": "validation", "num_bytes": 3876, "num_examples": 21}, {"name": "train", "num_bytes": 1092, "num_examples": 4}], "download_size": 166184960, "dataset_size": 45392}, {"config_name": "elementary_mathematics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 67369, "num_examples": 377}, {"name": "validation", "num_bytes": 8458, "num_examples": 40}, {"name": "train", "num_bytes": 1223, "num_examples": 4}], "download_size": 166184960, "dataset_size": 77050}, {"config_name": "human_aging", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 44398, "num_examples": 222}, {"name": "validation", "num_bytes": 4226, "num_examples": 22}, {"name": "train", "num_bytes": 774, "num_examples": 4}], "download_size": 166184960, "dataset_size": 49398}, {"config_name": "college_mathematics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 23739, "num_examples": 99}, {"name": "validation", "num_bytes": 2362, "num_examples": 10}, {"name": "train", "num_bytes": 1146, "num_examples": 4}], "download_size": 166184960, "dataset_size": 27247}, {"config_name": "high_school_psychology", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 155363, "num_examples": 544}, {"name": "validation", "num_bytes": 16538, "num_examples": 59}, {"name": "train", "num_bytes": 1618, "num_examples": 4}], "download_size": 166184960, "dataset_size": 173519}, {"config_name": "formal_logic", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 47829, "num_examples": 125}, {"name": "validation", "num_bytes": 5774, "num_examples": 13}, {"name": "train", "num_bytes": 1568, "num_examples": 4}], "download_size": 166184960, "dataset_size": 55171}, {"config_name": "high_school_statistics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 108742, "num_examples": 215}, {"name": "validation", "num_bytes": 9537, "num_examples": 22}, {"name": "train", "num_bytes": 1993, "num_examples": 4}], "download_size": 166184960, "dataset_size": 120272}, {"config_name": "international_law", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 52439, "num_examples": 120}, {"name": "validation", "num_bytes": 5918, "num_examples": 12}, {"name": "train", "num_bytes": 2017, "num_examples": 4}], "download_size": 166184960, "dataset_size": 60374}, {"config_name": "high_school_mathematics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 52702, "num_examples": 269}, {"name": "validation", "num_bytes": 5277, "num_examples": 28}, {"name": "train", "num_bytes": 826, "num_examples": 4}], "download_size": 166184960, "dataset_size": 58805}, {"config_name": "high_school_computer_science", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 43696, "num_examples": 99}, {"name": "validation", "num_bytes": 3088, "num_examples": 8}, {"name": "train", "num_bytes": 2463, "num_examples": 4}], "download_size": 166184960, "dataset_size": 49247}, {"config_name": "conceptual_physics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 38927, "num_examples": 234}, {"name": "validation", "num_bytes": 4154, "num_examples": 25}, {"name": "train", "num_bytes": 728, "num_examples": 4}], "download_size": 166184960, "dataset_size": 43809}, {"config_name": "miscellaneous", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 141981, "num_examples": 782}, {"name": "validation", "num_bytes": 13562, "num_examples": 85}, {"name": "train", "num_bytes": 565, "num_examples": 4}], "download_size": 166184960, "dataset_size": 156108}, {"config_name": "high_school_chemistry", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 56653, "num_examples": 202}, {"name": "validation", "num_bytes": 6725, "num_examples": 21}, {"name": "train", "num_bytes": 1074, "num_examples": 4}], "download_size": 166184960, "dataset_size": 64452}, {"config_name": "marketing", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 61240, "num_examples": 233}, {"name": "validation", "num_bytes": 7027, "num_examples": 24}, {"name": "train", "num_bytes": 1251, "num_examples": 4}], "download_size": 166184960, "dataset_size": 69518}, {"config_name": "professional_law", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1879598, "num_examples": 1533}, {"name": "validation", "num_bytes": 201226, "num_examples": 169}, {"name": "train", "num_bytes": 5085, "num_examples": 4}], "download_size": 166184960, "dataset_size": 2085909}, {"config_name": "management", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 19112, "num_examples": 102}, {"name": "validation", "num_bytes": 1598, "num_examples": 10}, {"name": "train", "num_bytes": 600, "num_examples": 4}], "download_size": 166184960, "dataset_size": 21310}, {"config_name": "college_physics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 29056, "num_examples": 101}, {"name": "validation", "num_bytes": 2958, "num_examples": 10}, {"name": "train", "num_bytes": 1164, "num_examples": 4}], "download_size": 166184960, "dataset_size": 33178}, {"config_name": "jurisprudence", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 32839, "num_examples": 107}, {"name": "validation", "num_bytes": 3438, "num_examples": 10}, {"name": "train", "num_bytes": 929, "num_examples": 4}], "download_size": 166184960, "dataset_size": 37206}, {"config_name": "world_religions", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 23974, "num_examples": 170}, {"name": "validation", "num_bytes": 2504, "num_examples": 18}, {"name": "train", "num_bytes": 508, "num_examples": 4}], "download_size": 166184960, "dataset_size": 26986}, {"config_name": "sociology", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 64513, "num_examples": 200}, {"name": "validation", "num_bytes": 6818, "num_examples": 21}, {"name": "train", "num_bytes": 1376, "num_examples": 4}], "download_size": 166184960, "dataset_size": 72707}, {"config_name": "us_foreign_policy", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 27391, "num_examples": 99}, {"name": "validation", "num_bytes": 2729, "num_examples": 10}, {"name": "train", "num_bytes": 1216, "num_examples": 4}], "download_size": 166184960, "dataset_size": 31336}, {"config_name": "high_school_macroeconomics", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 114578, "num_examples": 389}, {"name": "validation", "num_bytes": 12446, "num_examples": 42}, {"name": "train", "num_bytes": 927, "num_examples": 4}], "download_size": 166184960, "dataset_size": 127951}, {"config_name": "computer_security", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 26220, "num_examples": 99}, {"name": "validation", "num_bytes": 4178, "num_examples": 10}, {"name": "train", "num_bytes": 968, "num_examples": 4}], "download_size": 166184960, "dataset_size": 31366}, {"config_name": "moral_scenarios", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 367352, "num_examples": 894}, {"name": "validation", "num_bytes": 41184, "num_examples": 99}, {"name": "train", "num_bytes": 1598, "num_examples": 4}], "download_size": 166184960, "dataset_size": 410134}, {"config_name": "moral_disputes", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 105240, "num_examples": 345}, {"name": "validation", "num_bytes": 11732, "num_examples": 37}, {"name": "train", "num_bytes": 1196, "num_examples": 4}], "download_size": 166184960, "dataset_size": 118168}, {"config_name": "electrical_engineering", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 23901, "num_examples": 144}, {"name": "validation", "num_bytes": 2576, "num_examples": 15}, {"name": "train", "num_bytes": 801, "num_examples": 4}], "download_size": 166184960, "dataset_size": 27278}, {"config_name": "astronomy", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 45470, "num_examples": 151}, {"name": "validation", "num_bytes": 4482, "num_examples": 15}, {"name": "train", "num_bytes": 1672, "num_examples": 4}], "download_size": 166184960, "dataset_size": 51624}, {"config_name": "college_biology", "features": [{"name": "input", "dtype": "string"}, {"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 47319, "num_examples": 143}, {"name": "validation", "num_bytes": 4462, "num_examples": 15}, {"name": "train", "num_bytes": 1103, "num_examples": 4}], "download_size": 166184960, "dataset_size": 52884}]}
|
2023-02-02T02:38:44+00:00
|
95aff747a36bb7b065b40572ea6f4c37a00bae1e
|
Reindrob/civ
|
[
"license:unknown",
"region:us"
] |
2023-02-02T03:47:45+00:00
|
{"license": "unknown"}
|
2023-11-03T05:17:38+00:00
|
|
340c5c5150e6cf58d0391b18f550f942d094788b
|
nc33/MultiSpan_SQUAD
|
[
"license:mit",
"region:us"
] |
2023-02-02T04:37:24+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "num_span", "dtype": "int64"}, {"name": "label", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 141866486, "num_examples": 87599}, {"name": "validation", "num_bytes": 18219759, "num_examples": 10570}], "download_size": 16941350, "dataset_size": 160086245}}
|
2023-02-02T04:39:41+00:00
|
|
3dd821aa93262f450995831be12236f459c438ab
|
Isamu136/chess-gpt-data
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-02T04:39:50+00:00
|
{"license": "apache-2.0"}
|
2023-02-11T04:05:29+00:00
|
|
3b73579865a4275acd814d432c4b5256cabe1cd5
|
baitian/OA1pastelmix
|
[
"license:openrail",
"region:us"
] |
2023-02-02T06:10:27+00:00
|
{"license": "openrail"}
|
2023-03-19T10:27:47+00:00
|
|
60e77debf2aa36356353c0b6bde5964c1b235b37
|
pinakinathc/fscoco
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-02-02T06:14:13+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-05-12T14:52:21+00:00
|
|
71545b2db638dccc0ede5373590ceaa192bb4612
|
Kanr1u/test
|
[
"license:cc-by-nd-4.0",
"region:us"
] |
2023-02-02T06:42:10+00:00
|
{"license": "cc-by-nd-4.0"}
|
2023-02-02T06:42:10+00:00
|
|
7d2e052efaec29b2d3e4ea4acfb59bdfbad7ab14
|
# 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]
|
MUmerbhutta/prospect_aget
|
[
"region:us"
] |
2023-02-02T06:45:52+00:00
|
{}
|
2023-02-02T06:51:35+00:00
|
c32a69cc63da5f3b39b1914e2a8d3831844302d9
|
# Dataset Card for "nowiki_abstract_second_scrape_20230201"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jkorsvik/nowiki_abstract_second_scrape_20230201
|
[
"region:us"
] |
2023-02-02T07:39:55+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "date_scraped", "dtype": "string"}, {"name": "headline", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ingress", "dtype": "string"}, {"name": "article", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 841217948, "num_examples": 614918}], "download_size": 211286623, "dataset_size": 841217948}}
|
2023-02-02T07:40:15+00:00
|
4fdc4791fe8ea154876d9bc1ad1eecd140063287
|
# Dataset Card for "mgb2_audios_transcriptions_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
BelalElhossany/mgb2_audios_transcriptions_preprocessed
|
[
"region:us"
] |
2023-02-02T07:40:39+00:00
|
{"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1146187887.0, "num_examples": 5842}], "download_size": 1141969416, "dataset_size": 1146187887.0}}
|
2023-02-02T07:41:04+00:00
|
7f89fe670f7c2369a611d8b5a0095dfd3be39f13
|
# Dataset Card for "deneme"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nergis/deneme
|
[
"region:us"
] |
2023-02-02T08:50:29+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "sign1", "dtype": "string"}, {"name": "sign2", "dtype": "string"}, {"name": "sign3", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 6147164, "num_examples": 2452}], "download_size": 347074, "dataset_size": 6147164}}
|
2023-02-02T08:50:37+00:00
|
717d478b143836705356b6792834703ea62e29c2
|
davanstrien/twitterdemo
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:openrail",
"region:us"
] |
2023-02-02T10:16:25+00:00
|
{"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "FAKE DATASET"}
|
2023-02-02T10:24:59+00:00
|
|
83cbb59db3222087f30ab58d90aa4eee35ef8042
|
# Dataset Card for "aug-text-exps-v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
james-burton/aug-text-exps-v3
|
[
"region:us"
] |
2023-02-02T10:18:18+00:00
|
{"dataset_info": {"features": [{"name": "model_name", "dtype": "string"}, {"name": "predicted_class", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "narration", "dtype": "string"}, {"name": "values", "sequence": "string"}, {"name": "sign", "sequence": "string"}, {"name": "narrative_id", "dtype": "int32"}, {"name": "unique_id", "dtype": "int32"}, {"name": "classes_dict", "dtype": "string"}, {"name": "narrative_questions", "sequence": "string"}, {"name": "feature_nums", "sequence": "string"}, {"name": "ft_num2name", "dtype": "string"}, {"name": "old2new_ft_nums", "dtype": "string"}, {"name": "old2new_classes", "dtype": "string"}, {"name": "predicted_class_label", "dtype": "string"}, {"name": "class2name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8651458, "num_examples": 3280}, {"name": "validation", "num_bytes": 121591, "num_examples": 47}, {"name": "test", "num_bytes": 252513, "num_examples": 94}], "download_size": 2382860, "dataset_size": 9025562}}
|
2023-02-02T10:18:31+00:00
|
f6cb7963ca2f6db2529f0b8c7241595a87dd361e
|
nc33/MultiSpanQA
|
[
"license:mit",
"region:us"
] |
2023-02-02T10:29:15+00:00
|
{"license": "mit"}
|
2023-02-07T13:38:09+00:00
|
|
ed57db6f9caafce2d4708a19bd6c6d0e6e7980eb
|
# Dataset Card for "instruction-pilot-outputs-greedy"
This dataset contains model outputs generated from the human demonstrations provided in [`HuggingFaceH4/instruction-pilot-prompts`](https://huggingface.co/datasets/HuggingFaceH4/instruction-pilot-prompts).
To convert each language model into a dialogue agent, we prepended the following [LangChain prompt](https://github.com/hwchase17/langchain/blob/bfabd1d5c0bf536fdd1e743e4db8341e7dfe82a9/langchain/chains/conversation/prompt.py#LL4C21-L9C7) to each input:
```
The following is a friendly conversation between a human and an AI. \
The AI is talkative and provides lots of specific details from its context. \
If the AI does not know the answer to a question, it truthfully says it does not know.
Human: {input}
AI:
```
For reproducibility purposes, we used deterministic text generation (`temperature=0`) and set `max_new_tokens=100` (which is about the mean lenght of the Self-Instruct outputs).
|
HuggingFaceH4/instruction-pilot-outputs-greedy
|
[
"region:us"
] |
2023-02-02T12:35:22+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "outputs", "list": [{"name": "model", "dtype": "string"}, {"name": "output", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 243208, "num_examples": 375}], "download_size": 100726, "dataset_size": 243208}}
|
2023-02-08T15:28:45+00:00
|
71ad9853db7180b286e4ed94bb56214d08991790
|
# Dataset Card for Spider
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://yale-lily.github.io/spider
- **Repository:** https://github.com/taoyds/spider
- **Paper:** https://www.aclweb.org/anthology/D18-1425/
- **Point of Contact:** [Yale LILY](https://yale-lily.github.io/)
### Dataset Summary
Spider is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students
The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases
### Supported Tasks and Leaderboards
The leaderboard can be seen at https://yale-lily.github.io/spider
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
**What do the instances that comprise the dataset represent?**
Each instance is natural language question and the equivalent SQL query
**How many instances are there in total?**
**What data does each instance consist of?**
[More Information Needed]
### Data Fields
* **db_id**: Database name
* **question**: Natural language to interpret into SQL
* **query**: Target SQL query
* **query_toks**: List of tokens for the query
* **query_toks_no_value**: List of tokens for the query
* **question_toks**: List of tokens for the question
### Data Splits
**train**: 7000 questions and SQL query pairs
**dev**: 1034 question and SQL query pairs
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
[More Information Needed]
### Annotations
The dataset was annotated by 11 college students at Yale University
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
## Additional Information
The listed authors in the homepage are maintaining/supporting the dataset.
### Dataset Curators
[More Information Needed]
### Licensing Information
The spider dataset is licensed under
the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
[More Information Needed]
### Citation Information
```
@article{yu2018spider,
title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others},
journal={arXiv preprint arXiv:1809.08887},
year={2018}
}
```
### Contributions
Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset.
|
amitdanin/s3_spyder
|
[
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"text-to-sql",
"region:us"
] |
2023-02-02T14:00:34+00:00
|
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated", "machine-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "spider-1", "pretty_name": "Spider", "tags": ["text-to-sql"], "dataset_info": {"features": [{"name": "db_id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "query_toks", "sequence": "string"}, {"name": "query_toks_no_value", "sequence": "string"}, {"name": "question_toks", "sequence": "string"}], "config_name": "spider", "splits": [{"name": "train", "num_bytes": 4743786, "num_examples": 7000}, {"name": "validation", "num_bytes": 682090, "num_examples": 1034}], "download_size": 99736136, "dataset_size": 5425876}}
|
2023-02-02T15:54:28+00:00
|
2aef371381065170c0737cbaaf3a1f377325878b
|
HarborYuan/Few-Shot-Class-Incremental-Learning
|
[
"license:other",
"region:us"
] |
2023-02-02T14:08:28+00:00
|
{"license": "other"}
|
2023-02-02T15:03:29+00:00
|
|
caa03818e55f4c01b428182486355eed9efd9547
|
# Dataset Card for "FGVC_Aircraft_test_facebook_opt_2.7b_Attributes_Caption_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/FGVC_Aircraft_test_facebook_opt_2.7b_Attributes_Caption_ns_3333
|
[
"region:us"
] |
2023-02-02T14:18:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 299298598.375, "num_examples": 3333}, {"name": "fewshot_1_bs_16", "num_bytes": 300147806.375, "num_examples": 3333}, {"name": "fewshot_3_bs_16", "num_bytes": 301862835.375, "num_examples": 3333}], "download_size": 891920497, "dataset_size": 901309240.125}}
|
2023-02-02T15:11:35+00:00
|
3fc4076326aee0a19c3bcf349a08d44f69c2ab2b
|
# Dataset Card for "FGVC_Aircraft_test_facebook_opt_2.7b_Visclues_ns_3333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/FGVC_Aircraft_test_facebook_opt_2.7b_Visclues_ns_3333
|
[
"region:us"
] |
2023-02-02T14:26:42+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 299564852.375, "num_examples": 3333}, {"name": "fewshot_1_bs_16", "num_bytes": 300685275.375, "num_examples": 3333}, {"name": "fewshot_3_bs_16", "num_bytes": 302937771.375, "num_examples": 3333}], "download_size": 892471687, "dataset_size": 903187899.125}}
|
2023-02-02T15:37:15+00:00
|
a1d88d87e411ffa4f62d42d2b141916a3da72ddb
|
# Dataset Card for "fortnite_characters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
olly4/fortnite_characters
|
[
"region:us"
] |
2023-02-02T14:29:15+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 209672213.69, "num_examples": 1638}], "download_size": 201739084, "dataset_size": 209672213.69}}
|
2023-02-03T14:45:09+00:00
|
80aa0b18c36ffc90f3feb0b929e18922529d0e2b
|
# Dataset Card for "mgb2_audios_transcriptions_prepared"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
BelalElhossany/mgb2_audios_transcriptions_prepared
|
[
"region:us"
] |
2023-02-02T14:49:16+00:00
|
{"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}, {"name": "input_values", "sequence": "float32"}, {"name": "input_length", "dtype": "int64"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3439273371.75, "num_examples": 5842}], "download_size": 3255737742, "dataset_size": 3439273371.75}}
|
2023-02-02T14:56:06+00:00
|
157c134d0eb758bc8fdf097a5eaf487065578f95
|
# Dataset Card for "test_dataset_cogapp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
The first row of the dataset looks like
<!-- [[[cog
from datasets import load_dataset
import json
ds = load_dataset("davanstrien/test_dataset_cogapp")
data = ds['train'][0]
example = json.dumps({k: str(v) for k,v in data.items()}, indent=2)
cog.out(
"```\n{}\n```".format(example))
)]]] -->
```
{
"file": "pst_fenske_ver02_data_sn84026497_00280776129_1880042101_0834_002_6_96.jpg",
"image": "<PIL.JpegImagePlugin.JpegImageFile image mode=L size=388x395 at 0x11AF00990>",
"label": "0",
"pub_date": "1880-04-21 00:00:00",
"page_seq_num": "834",
"edition_seq_num": "1",
"batch": "pst_fenske_ver02",
"lccn": "sn84026497",
"box": "[0.649412214756012, 0.6045778393745422, 0.8002520799636841, 0.7152365446090698]",
"score": "0.9609346985816956",
"ocr": "H. II. IIASLKT & SOXN, Dealers in General Merchandise In New Store Room nt HASLET'S COS ITERS, 'JTionoMtii, ln. .Tau'y 1st, 1?0.",
"place_of_publication": "Tionesta, Pa.",
"geographic_coverage": "['Pennsylvania--Forest--Tionesta']",
"name": "The Forest Republican. [volume]",
"publisher": "Ed. W. Smiley",
"url": "https://news-navigator.labs.loc.gov/data/pst_fenske_ver02/data/sn84026497/00280776129/1880042101/0834/002_6_96.jpg",
"page_url": "https://chroniclingamerica.loc.gov/data/batches/pst_fenske_ver02/data/sn84026497/00280776129/1880042101/0834.jp2"
}
```
<!-- [[[end]]] -->
<!-- [[[cog
from auto_dataset_card.core import generate_label_breakdown_tables, get_label_counts
ds = load_dataset("davanstrien/test_dataset_cogapp")
data = get_label_counts(ds)
cog.out(
f"""
# Label breakdowns \n
```
{data}
```
""")
]]] -->
# Label breakdowns
```
{'train': {'text-only': 376, 'illustrations': 173}}
```
<!-- [[[end]]] -->
<!-- [[[cog
from auto_dataset_card.core import generate_label_breakdown_tables, get_label_counts
ds = load_dataset("davanstrien/test_dataset_cogapp")
data = get_label_counts(ds)
tables = generate_label_breakdown_tables(data)
split = tables[0][0]
table = tables[0][1]
cog.out(
f"""
# Label breakdown table for split: {split} \n
{table}
""")
]]] -->
# Label breakdown table for split: train
| Label | Count | Percentage |
|---------------|---------|--------------|
| text-only | 376 | 68.49% |
| illustrations | 173 | 31.51% |
<!-- [[[end]]] -->
|
davanstrien/autogenerated-dataset-card
|
[
"size_categories:n<1K",
"region:us"
] |
2023-02-02T14:53:53+00:00
|
{"size_categories": ["n<1K"], "dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "text-only", "1": "illustrations"}}}}, {"name": "pub_date", "dtype": "timestamp[ns]"}, {"name": "page_seq_num", "dtype": "int64"}, {"name": "edition_seq_num", "dtype": "int64"}, {"name": "batch", "dtype": "string"}, {"name": "lccn", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "score", "dtype": "float64"}, {"name": "ocr", "dtype": "string"}, {"name": "place_of_publication", "dtype": "string"}, {"name": "geographic_coverage", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "publisher", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "page_url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48233952, "num_examples": 549}], "download_size": 48027719, "dataset_size": 48233952}}
|
2023-02-15T10:35:21+00:00
|
43e156a93b5a8c5e8af085dea919d4d9a5f84669
|
<style>
.prose img {
display: inline;
margin: 0 6px !important;
}
.prose table {
max-width: 320px;
margin: 0;
}
</style>
# Badges
A set of badges you can use anywhere. Just update the anchor URL to point to the correct action for your Space. Light or dark background with 4 sizes available: small, medium, large, and extra large.
## How to use?
- With markdown, just copy the badge from: https://huggingface.co/datasets/huggingface/badges/blob/main/README.md?code=true
- With HTML, inspect this page with your web browser and copy the outer html.
## Available sizes
| Small | Medium | Large | Extra large |
| ------------- | :-----------: | ------------- | ------------- |
| 20px (height) | 24px (height) | 36px (height) | 48px (height) |
## Paper page
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
[](https://huggingface.co/papers)
## Deploy on Spaces
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
[](https://huggingface.co/new-space)
## Duplicate this Space
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
[](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery?duplicate=true)
## Open in HF Spaces
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
## Open a Discussion
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
## Share to Community
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
[](https://huggingface.co/spaces)
## Sign in with Hugging Face
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
[](https://huggingface.co/)
## Open a Pull Request
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
[](https://huggingface.co/spaces/victor/ChatUI/discussions)
## Subscribe to PRO
[](https://huggingface.co/subscribe/pro)
[](https://huggingface.co/subscribe/pro)
[](https://huggingface.co/subscribe/pro)
[](https://huggingface.co/subscribe/pro)
[](https://huggingface.co/subscribe/pro)
[](https://huggingface.co/subscribe/pro)
[](https://huggingface.co/subscribe/pro)
[](https://huggingface.co/subscribe/pro)
## Follow me on HF
[](https://huggingface.co/Chunte)
[](https://huggingface.co/Chunte)
[](https://huggingface.co/Chunte)
[](https://huggingface.co/Chunte)
[](https://huggingface.co/Chunte)
[](https://huggingface.co/Chunte)
[](https://huggingface.co/Chunte)
[](https://huggingface.co/Chunte)
## Model on HF
[](https://huggingface.co/models)
[](https://huggingface.co/models)
[](https://huggingface.co/models)
[](https://huggingface.co/models)
[](https://huggingface.co/models)
[](https://huggingface.co/models)
[](https://huggingface.co/models)
[](https://huggingface.co/models)
## Dataset on HF
[](https://huggingface.co/datasets)
[](https://huggingface.co/datasets)
[](https://huggingface.co/datasets)
[](https://huggingface.co/datasets)
[](https://huggingface.co/datasets)
[](https://huggingface.co/datasets)
[](https://huggingface.co/datasets)
[](https://huggingface.co/datasets)
## Powered by Hugging Face
[](https://huggingface.co)
[](https://huggingface.co)
|
huggingface/badges
|
[
"license:mit",
"region:us"
] |
2023-02-02T14:55:23+00:00
|
{"license": "mit", "thumbnail": "https://huggingface.co/datasets/huggingface/badges/resolve/main/badges-thumbnail.png"}
|
2024-01-19T18:27:34+00:00
|
4b8fc2bb85b5fa519cbbb9de22d102547532c410
|
## Metrics
|Metric/Model| ChatGPT | sanagnos/galactica-6.7b-finetuned | NeoX-Soda |
|---|---|---|---|
|rouge1| 0.2865 | 0.1513 ||
|Rouge2| 0.05863 | 0.0311 ||
|rougeL| 0.1519 | 0.1065 ||
|rougeLsum| 0.1636 | 0.1076 ||
|
xzyao/HC3-Evaluation
|
[
"region:us"
] |
2023-02-02T15:02:34+00:00
|
{}
|
2023-02-02T19:53:41+00:00
|
f301e669c17d0b2487c0ce122926948432c6a336
|
VASVASVAS/vae
|
[
"license:openrail",
"region:us"
] |
2023-02-02T15:12:03+00:00
|
{"license": "openrail"}
|
2023-02-03T20:13:23+00:00
|
|
a65fe3d0fbbe5ed1be73d41cb9addd7557a8a374
|
dpredrag/data
|
[
"license:creativeml-openrail-m",
"region:us"
] |
2023-02-02T15:39:28+00:00
|
{"license": "creativeml-openrail-m"}
|
2023-02-02T15:39:53+00:00
|
|
cc1faf907fef9646be55abeaa10153ee710e8eac
|
bruno-cotrim/arch-max-blender-proj
|
[
"license:apache-2.0",
"region:us"
] |
2023-02-02T16:38:29+00:00
|
{"license": "apache-2.0"}
|
2023-02-02T17:02:39+00:00
|
|
315ddd7a1ccce96784f505def5828f9129f7ac4c
|
# Dataset Card for "dataset_playground"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ernestum/dataset_playground
|
[
"region:us"
] |
2023-02-02T16:43:05+00:00
|
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float64"}}, {"name": "acts", "sequence": {"sequence": "float64"}}, {"name": "infos", "list": [{"name": "a", "dtype": "int64"}]}, {"name": "terminal", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 225, "num_examples": 2}], "download_size": 2240, "dataset_size": 225}}
|
2023-02-02T16:51:16+00:00
|
81c08e854d125a0b87834d054900dd4a15ab299c
|
# Dataset Card for "OxfordPets_test_facebook_opt_350m_Visclues_ns_100_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_350m_Visclues_ns_100_random
|
[
"region:us"
] |
2023-02-02T18:16:28+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 3560398.0, "num_examples": 100}], "download_size": 3506026, "dataset_size": 3560398.0}}
|
2023-02-02T18:16:31+00:00
|
caea6c10f9c38a2296d751bb51ad2fbf430c7ac4
|
# Dataset Card for "OxfordPets_test_facebook_opt_350m_Visclues_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_350m_Visclues_ns_3669_random
|
[
"region:us"
] |
2023-02-02T18:20:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 122803649.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 125490550.375, "num_examples": 3669}], "download_size": 242181633, "dataset_size": 248294199.75}}
|
2023-02-02T18:25:41+00:00
|
58870a0b0a295d6cbe3b3ff2478620eb563a252d
|
# Dataset Card for "OxfordPets_test_facebook_opt_1.3b_Visclues_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_1.3b_Visclues_ns_3669_random
|
[
"region:us"
] |
2023-02-02T18:39:27+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_5_bs_16", "num_bytes": 128177014.375, "num_examples": 3669}, {"name": "fewshot_1_bs_16", "num_bytes": 122802439.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 125493335.375, "num_examples": 3669}], "download_size": 364477060, "dataset_size": 376472789.125}}
|
2023-02-02T21:54:03+00:00
|
d8ab01b071ae5d372c65725855a4e3e4f62869a8
|
# Dataset Card for "patched_test_p_100_m1_predictions_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roa7n/patched_test_p_100_m1_predictions_v2
|
[
"region:us"
] |
2023-02-02T19:34:30+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1285013913, "num_examples": 2224950}], "download_size": 111868027, "dataset_size": 1285013913}}
|
2023-02-02T19:34:49+00:00
|
c481d718075a6b441f95e053fd3b361d31317e64
|
cmauck10/wall-robot-navigation
|
[
"license:unknown",
"region:us"
] |
2023-02-02T19:45:18+00:00
|
{"license": "unknown"}
|
2023-02-02T19:45:56+00:00
|
|
cdb8c63aca5b37e6b9dfcbf103ce227b9bd85bcf
|
# John Kafka Artstyle LoRA
# Use Cases
The LoRA is in itself very compatible with the most diverse model. However, it is most effective when used with Kenshi or AbyssOrangeMix2.
The LoRA itself was trained with the token: ```skistyle```.
The models mentioned right now
1. AbyssOrangeMix2 from [WarriorMama777](https://huggingface.co/WarriorMama777/OrangeMixs)
2. Kenshi Model from [Luna](https://huggingface.co/SweetLuna/Kenshi)
## Strength
I would personally use these strength with the assosiated model:
Soft-Version:
- 0.85-1 for AbyssOrangeMix2
- 0.75-0.9 for Kenshi
Hard-Version:
- 0.6-0.8 for AbyssOrangeMix2
- 0.55-0.75 for Kenshi
# Showcase
**Example 1**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/John_Kafka_LoRA/resolve/main/preview/preview%20(2).png"/>
```
skistyle,
1girl, small cute red nose, animal_ears, artist_name, bangs, some freckles, black_hair, black_skirt, blue_ribbon, smiling, solo, looking at viewer, collared_shirt, flower, fox_ears, grey_flower, hair_flower, hair_ornament, highres, league_of_legends, long_hair, looking_at_viewer, neck_ribbon, orange_eyes, pleated_skirt, ribbon, shirt, sitting, skirt, solo, white_shirt, shy
Steps: 32, Sampler: Euler a, CFG scale: 7
```
**Example 2**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/John_Kafka_LoRA/resolve/main/preview/preview%20(3).png"/>
```
skistyle,
1girl, small cute red nose, animal_ears, artist_name, bangs, some freckles, black_hair, black_skirt, blue_ribbon, smiling, solo, looking at viewer, collared_shirt, flower, fox_ears, grey_flower, hair_flower, hair_ornament, highres, league_of_legends, long_hair, looking_at_viewer, neck_ribbon, orange_eyes, pleated_skirt, ribbon, shirt, sitting, skirt, solo, white_shirt, shy
Steps: 32, Sampler: Euler a, CFG scale: 7
```
**Example 3**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/John_Kafka_LoRA/resolve/main/preview/preview%20(4).png"/>
```
skistyle,
1girl, (masterpiece:1.2), (highly detailed), ((best quality)), (ultra-detailed)
Steps: 32, Sampler: Euler a, CFG scale: 7
```
# License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
Nerfgun3/John_Kafka_LoRA
|
[
"language:en",
"license:creativeml-openrail-m",
"stable-diffusion",
"text-to-image",
"image-to-image",
"region:us"
] |
2023-02-02T19:48:57+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "thumbnail": "https://huggingface.co/datasets/Nerfgun3/John_Kafka_LoRA/resolve/main/preview/preview%20(1).png", "tags": ["stable-diffusion", "text-to-image", "image-to-image"], "inference": false}
|
2023-02-02T19:58:09+00:00
|
73d604e992805542fcb43bac0224e01307c19a6d
|
# Dataset Card for toxic-detection-testset-perturnations
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset a test set for toxic detection that contains both clean data and it's perturbed version with human-written perturbations online.
In addition, our dataset can be used to benchmark misspelling correctors as well.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
```
{
"clean_version": "this is pretty much exactly how i feel damn",
"perturbed_version": "this is pretty much exactly how i feel daaammnn",
"toxicity": 0.7,
"obscene": 0.7,
"sexual_explicit": 0,
"identity_attack": 0,
...
"insult": 0.2,
"quality_mean": 4
}
```
### Data Fields
This dataset is derived from the [Jigsaw data](https://www.kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification/data). Hence, it keeps all the useful metrics and attributes.
**Main**
* clean_version
* perturbed_version
**Metrics**
* toxicity
* severe_toxicity
* obscene
* threat
* insult
* identity_attack
* sexual_explicit
**Identity attributes**
* male
* female
* transgender
* other_gender
* heterosexual
* homosexual_gay_or_lesbian
* bisexual
* other_sexual_orientation
* christian
* jewish
* muslim
* hindu
* buddhist
* atheist
* other_religion
* black
* white
* asian
* latino
* other_race_or_ethnicity
* physical_disability
* intellectual_or_learning_disability
* psychiatric_or_mental_illness
* other_disability
### Data Splits
test: 1339
## 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?
US Amazon MTurk workers with HIT Approval Rate greater than 98%, and Number of HITs approved greater than 1000.
### 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]
|
yiran223/toxic-detection-testset-perturbations
|
[
"size_categories:10K<n<100K",
"language:en",
"region:us"
] |
2023-02-02T19:54:51+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"]}
|
2023-02-20T22:59:19+00:00
|
67932c434f24ffd1b4c586fd0de7ca75f074471c
|
# Dataset Card for "output_from_hpqa_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Reza-Madani/output_from_hpqa_test
|
[
"region:us"
] |
2023-02-02T20:03:20+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "facts", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3660168, "num_examples": 7405}], "download_size": 2430550, "dataset_size": 3660168}}
|
2023-02-02T20:03:26+00:00
|
614d4269d500aeb40c0bedfd62320992bf7928d9
|
# Liang Xing Artstyle LoRA
# Use Cases
The LoRA is in itself very compatible with the most diverse model. However, it is most effective when used with Kenshi or AbyssOrangeMix2.
The LoRA itself was trained with the token: ```skistyle```.
The model mentioned right now
1. AbyssOrangeMix2 from [WarriorMama777](https://huggingface.co/WarriorMama777/OrangeMixs)
2. Kenshi Model from [Luna](https://huggingface.co/SweetLuna/Kenshi)
## Strength
I would personally use these strength with the assosiated model:
- 0.8-1 for AbyssOrangeMix2
- 0.7-0.85 for Kenshi
# Showcase
**Example 1**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/Liang_Xing_LoRA/resolve/main/preview/preview%20(3).png"/>
```
skistyle,
miku hatsune, masterpiece, best quality, 1girl, blush, aqua eyes, cap, closed mouth, earrings, hat, hoop earrings, jewelry, looking at viewer, shirt, simple background, solo, upper body, yellow shirt
Steps: 32, Sampler: Euler a, CFG scale: 7
```
**Example 2**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/Liang_Xing_LoRA/resolve/main/preview/preview%20(1).png"/>
```
skistyle,
multiple girls, 7girls, ((maid outfits)), fully clothed, (small breasts:1.2), witch hat, eyepatch, twintails, long hair, hat, hair ornament, blonde hair, black hair, gloves, white hair, looking at viewer, red eyes, sky, elbow gloves, from below, sfw, black gloves, thighs, hairpin, brown hair, hair stick, purple headwear
Steps: 32, Sampler: Euler a, CFG scale: 7
```
**Example 3**
<img alt="Showcase" src="https://huggingface.co/datasets/Nerfgun3/Liang_Xing_LoRA/resolve/main/preview/preview%20(4).png"/>
```
skistyle,
kaya izumi, masterpiece, best quality, 1girl, blush, aqua eyes, cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt
Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7
```
# License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
Nerfgun3/Liang_Xing_LoRA
|
[
"language:en",
"license:creativeml-openrail-m",
"stable-diffusion",
"text-to-image",
"image-to-image",
"region:us"
] |
2023-02-02T20:22:47+00:00
|
{"language": ["en"], "license": "creativeml-openrail-m", "thumbnail": "https://huggingface.co/datasets/Nerfgun3/Liang_Xing_LoRA/resolve/main/preview/preview%20(2).png", "tags": ["stable-diffusion", "text-to-image", "image-to-image"], "inference": false}
|
2023-02-02T20:35:16+00:00
|
6ca9f8160df167a7a679977158728726c3b2aa27
|
# Dataset Card for identities
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases and Limitations](#discussion-of-biases-and-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://huggingface.co/spaces/tti-bias/stable-bias
- **Repository:** Forthcoming
- **Paper:** Forthcoming
- **Leaderboard:**
- **Point of Contact:** Forthcoming
### Dataset Summary
<span style="color:blue" font-size:smaller><b>🏗️ WORK IN PROGRESS</b></span>
<span style="color:red" font-size:smaller><b>⚠️ DISCLAIMER: The images in this dataset were generated by text-to-image systems and may depict offensive stereotypes or contain explicit content.</b></span>
The *Identities* dataset is a collection of computer-generated images generated using Text-to-Image (TTI) systems.
In order to generate a diverse set of prompts to evaluate the system outputs’ variation across dimensions of interest, we use the pattern **`Photo portrait of a [X] [Y] at work`**, where **[X]** spans ... and **[Y]** spans ....
```python
["American_Indian", "Black"]
```
```python
["woman", "man", "non-binary", "no_gender_specified"]
# no_gender_specified corresponds to a value of "person" for **[Y]**
```
Every prompt is used to generate images from the following models: **Stable Diffusion v.1.4, Stable Diffusion v.2., and Dall-E 2**
### Supported Tasks
This dataset can be used to evaluate the output space of TTI systems, particularly against the backdrop of societal representativeness.
### Languages
The prompts that generated the images are all in US-English.
## Dataset Structure
The dataset is stored in `parquet` format and contains 2040 rows which can be loaded like so:
```python
from datasets import load_dataset
dataset = load_dataset("tti-bias/professions", split="train")
```
### Data Fields
Each row corresponds to the output of a TTI system and looks as follows:
```python
{
'ethnicity': 'South_Asian',
'gender': 'man',
'no': 1,
'image_path': 'Photo_portrait_of_a_South_Asian_man_at_work/Photo_portrait_of_a_South_Asian_man_at_work_1.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512>,
'model': 'SD_2'
}
```
### Data Splits
All the data is contained within the `train` split. As such, the dataset contains practically no splits.
## Dataset Creation
### Curation Rationale
This dataset was created to explore the output characteristics of TTI systems from the vantage point of societal characteristics of interest.
### Source Data
#### Initial Data Collection and Normalization
The data was generated using the [DiffusionPipeline]() from Hugging Face:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
images = pipeline(prompt="Photo portrait of an African woman at work", num_images_per_prompt=9).images
```
### Personal and Sensitive Information
Generative models trained on large datasets have been shown to memorize part of their training sets (See e.g.: [(Carlini et al. 2023)](https://arxiv.org/abs/2301.13188)) and the people generated could theoretically bear resemblance to real people.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases and Limitations
At this point in time, the data is limited to images generated using English prompts and a set of professions sourced form the U.S. Bureau of Labor Statistics (BLS), which also provides us with additional information such as the demographic characteristics and salaries of each profession. While this data can also be leveraged in interesting analyses, it is currently limited to the North American context.
## Additional Information
### Licensing Information
The dataset is licensed under the Creative Commons [Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/) license.
### Citation Information
If you use this dataset in your own work, please consider citing:
```json
@article{stable-bias-authors-2023,
author = {Anonymous Authors},
title = {Stable Bias: Analyzing Societal Representations in Diffusion Models},
year = {2023},
}
```
|
stable-bias/identities
|
[
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2301.13188",
"region:us"
] |
2023-02-02T20:24:46+00:00
|
{"language": ["en"], "license": "cc-by-sa-4.0", "dataset_info": {"features": [{"name": "ethnicity", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "model", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 585336673, "num_examples": 2040}], "download_size": 465986042, "dataset_size": 585336673}}
|
2023-08-21T17:34:57+00:00
|
49b2608830c6c6e5b07f900363770d7b5b15a984
|
# Dataset Card for professions
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases and Limitations](#discussion-of-biases-and-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://huggingface.co/spaces/tti-bias/stable-bias
- **Repository:** Forthcoming
- **Paper:** Forthcoming
- **Leaderboard:**
- **Point of Contact:** Forthcoming
### Dataset Summary
<span style="color:blue" font-size:smaller><b>🏗️ WORK IN PROGRESS</b></span>
<span style="color:red" font-size:smaller><b>⚠️ DISCLAIMER: The images in this dataset were generated by text-to-image systems and may depict offensive stereotypes or contain explicit content.</b></span>
The *Professions* dataset is a collection of computer-generated images generated using Text-to-Image (TTI) systems.
In order to generate a diverse set of prompts to evaluate the system outputs’ variation across dimensions of interest, we use the pattern **`Photo portrait of a [X]`**, where X spans the professional attribute that we focus our analysis on, i.e. the name of the profession.
For the professions, we rely on a list of 146 occupations taken from the U.S. Bureau of Labor Statistics, and they are:
```python
['accountant', 'aerospace_engineer', 'aide', 'air_conditioning_installer', 'architect', 'artist', 'author',
'baker', 'bartender', 'bus_driver', 'butcher', 'career_counselor', 'carpenter', 'carpet_installer', 'cashier',
'ceo', 'childcare_worker', 'civil_engineer', 'claims_appraiser', 'cleaner', 'clergy', 'clerk', 'coach',
'community_manager', 'compliance_officer', 'computer_programmer', 'computer_support_specialist', 'computer_systems_analyst',
'construction_worker', 'cook', 'correctional_officer', 'courier', 'credit_counselor', 'customer_service_representative',
'data_entry_keyer', 'dental_assistant', 'dental_hygienist', 'dentist', 'designer', 'detective', 'director', 'dishwasher',
'dispatcher', 'doctor', 'drywall_installer', 'electrical_engineer', 'electrician', 'engineer', 'event_planner',
'executive_assistant', 'facilities_manager', 'farmer', 'fast_food_worker', 'file_clerk', 'financial_advisor',
'financial_analyst', 'financial_manager', 'firefighter', 'fitness_instructor', 'graphic_designer', 'groundskeeper',
'hairdresser', 'head_cook', 'health_technician', 'host', 'hostess', 'industrial_engineer', 'insurance_agent',
'interior_designer', 'interviewer', 'inventory_clerk', 'it_specialist', 'jailer', 'janitor', 'laboratory_technician',
'language_pathologist', 'lawyer', 'librarian', 'logistician', 'machinery_mechanic', 'machinist', 'maid', 'manager',
'manicurist', 'market_research_analyst', 'marketing_manager', 'massage_therapist', 'mechanic', 'mechanical_engineer',
'medical_records_specialist', 'mental_health_counselor', 'metal_worker', 'mover', 'musician', 'network_administrator',
'nurse', 'nursing_assistant', 'nutritionist', 'occupational_therapist', 'office_clerk', 'office_worker', 'painter',
'paralegal', 'payroll_clerk', 'pharmacist', 'pharmacy_technician', 'photographer', 'physical_therapist', 'pilot',
'plane_mechanic', 'plumber', 'police_officer', 'postal_worker', 'printing_press_operator', 'producer', 'psychologist',
'public_relations_specialist', 'purchasing_agent', 'radiologic_technician', 'real_estate_broker', 'receptionist',
'repair_worker', 'roofer', 'sales_manager', 'salesperson', 'school_bus_driver', 'scientist', 'security_guard',
'sheet_metal_worker', 'singer', 'social_assistant', 'social_worker', 'software_developer', 'stocker', 'stubborn',
'supervisor', 'taxi_driver', 'teacher', 'teaching_assistant', 'teller', 'therapist', 'tractor_operator', 'truck_driver',
'tutor', 'underwriter', 'veterinarian', 'waiter', 'waitress', 'welder', 'wholesale_buyer', 'writer']
```
Every prompt is used to generate images from the following models: **Stable Diffusion v.1.4, Stable Diffusion v.2., and Dall-E 2**
### Supported Tasks
This dataset can be used to evaluate the output space of TTI systems, particularly against the backdrop of societal representativeness.
### Languages
The prompts that generated the images are all in US-English.
## Dataset Structure
The dataset is stored in `parquet` format and contains 94,500 rows which can be loaded like so:
```python
from datasets import load_dataset
dataset = load_dataset("tti-bias/professions", split="train")
```
### Data Fields
Each row corresponds to the output of a TTI system and looks as follows:
```python
{
'adjective': 'ambitious',
'profession': 'butcher',
'no': 4,
'image_path': 'Photo_portrait_of_an_ambitious_butcher/Photo_portrait_of_an_ambitious_butcher_4.jpg',
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512>,
'model': 'SD_14'
}
```
### Data Splits
All the data is contained within the `train` split. As such, the dataset contains practically no splits.
## Dataset Creation
### Curation Rationale
This dataset was created to explore the output characteristics of TTI systems from the vantage point of societal characteristics of interest.
### Source Data
#### Initial Data Collection and Normalization
The data was generated using the [DiffusionPipeline]() from Hugging Face:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
images = pipeline(prompt="Photo portrait of a bus driver at work", num_images_per_prompt=9).images
```
### Personal and Sensitive Information
Generative models trained on large datasets have been shown to memorize part of their training sets (See e.g.: [(Carlini et al. 2023)](https://arxiv.org/abs/2301.13188)) and the people generated could theoretically bear resemblance to real people.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases and Limitations
At this point in time, the data is limited to images generated using English prompts and a set of professions sourced form the U.S. Bureau of Labor Statistics (BLS), which also provides us with additional information such as the demographic characteristics and salaries of each profession. While this data can also be leveraged in interesting analyses, it is currently limited to the North American context.
## Additional Information
### Licensing Information
The dataset is licensed under the Creative Commons [Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/) license.
### Citation Information
If you use this dataset in your own work, please consider citing:
```json
@article{stable-bias-authors-2023,
author = {Anonymous Authors},
title = {Stable Bias: Analyzing Societal Representations in Diffusion Models},
year = {2023},
}
```
|
stable-bias/professions
|
[
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2301.13188",
"region:us"
] |
2023-02-02T20:49:02+00:00
|
{"language": ["en"], "license": "cc-by-sa-4.0", "dataset_info": {"features": [{"name": "adjective", "dtype": "string"}, {"name": "profession", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "model", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3088839692.5, "num_examples": 94500}], "download_size": 3075495491, "dataset_size": 3088839692.5}}
|
2023-08-21T14:16:10+00:00
|
0ea7a6ebb26a4dc5af78ffd133627df819beb7f8
|
Reddit Demo dataset
|
AlhitawiMohammed22/reddit-demo
|
[
"license:mit",
"region:us"
] |
2023-02-02T20:59:08+00:00
|
{"license": "mit"}
|
2023-06-21T12:59:08+00:00
|
8d561a7d8a0fb43dba0b05c5460b8e03c7cd1f72
|
# Dataset Card for "bookcorpus_compact_1024_shard7_of_10_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
saibo/bookcorpus_compact_1024_shard7_of_10_meta
|
[
"region:us"
] |
2023-02-02T21:00:17+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7989189823, "num_examples": 61605}], "download_size": 1776852460, "dataset_size": 7989189823}}
|
2023-02-02T21:03:05+00:00
|
505b53a0949d720366c66431a1500a09a751c692
|
# Dataset Card for "OxfordPets_test_facebook_opt_1.3b_Attributes_Caption_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_1.3b_Attributes_Caption_ns_3669_random
|
[
"region:us"
] |
2023-02-02T21:08:52+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 122168789.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 124212925.375, "num_examples": 3669}], "download_size": 241370243, "dataset_size": 246381714.75}}
|
2023-02-02T21:35:49+00:00
|
2637c6a1f1d06a33e9d1030277444a98e6c3a509
|
# Dataset Card for "OxfordPets_test_facebook_opt_2.7b_Attributes_Caption_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_2.7b_Attributes_Caption_ns_3669_random
|
[
"region:us"
] |
2023-02-02T21:15:48+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 122169348.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 124213174.375, "num_examples": 3669}], "download_size": 241364874, "dataset_size": 246382522.75}}
|
2023-02-02T21:45:39+00:00
|
ae179307b6fdd736a56cbf203e46a7fe83395a37
|
https://github.com/rudinger/defeasible-nli
```
@inproceedings{rudinger2020thinking,
title={Thinking like a skeptic:
feasible inference in natural language},
author={Rudinger, Rachel and Shwartz, Vered and Hwang, Jena D and Bhagavatula, Chandra and Forbes, Maxwell and Le Bras, Ronan and Smith, Noah A and Choi, Yejin},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2020},
pages={4661--4675},
year={2020}
}
```
|
metaeval/defeasible-nli
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-02-02T21:21:26+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"]}
|
2023-06-22T13:09:34+00:00
|
4d67923c5740d1530f54ea7c8f6544906006499f
|
# Dataset Card for "professions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
SDbiaseval/professions
|
[
"region:us"
] |
2023-02-02T21:23:21+00:00
|
{"dataset_info": {"features": [{"name": "adjective", "dtype": "string"}, {"name": "profession", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "image_path", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "model", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3088839692.5, "num_examples": 94500}], "download_size": 3075495491, "dataset_size": 3088839692.5}}
|
2023-02-03T20:16:58+00:00
|
161487e2247c3fd597399195fd9f4d435f742344
|
# Dataset Card for "output_from_hpqa_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Reza-Madani/output_from_hpqa_validation
|
[
"region:us"
] |
2023-02-02T21:26:04+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "facts", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 4546213, "num_examples": 9045}], "download_size": 3008407, "dataset_size": 4546213}}
|
2023-02-02T21:26:11+00:00
|
b0886e7baf21acf5b3b6805daf40406322b17a56
|
# Dataset Card for "OxfordPets_test_facebook_opt_2.7b_Visclues_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_2.7b_Visclues_ns_3669_random
|
[
"region:us"
] |
2023-02-02T21:28:56+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 122804265.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 125494297.375, "num_examples": 3669}], "download_size": 242177732, "dataset_size": 248298562.75}}
|
2023-02-02T22:07:44+00:00
|
0a1d52d63ccdb90ee0a329c42a2ca6fb771c73c8
|
# Dataset Card for "tweets_pt_sentiment_analysis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ricardo-filho/tweets_pt_sentiment_analysis
|
[
"region:us"
] |
2023-02-02T21:35:41+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 80942752, "num_examples": 819346}, {"name": "validation", "num_bytes": 824575, "num_examples": 8360}, {"name": "test", "num_bytes": 814723, "num_examples": 8360}], "download_size": 61192823, "dataset_size": 82582050}}
|
2023-02-02T21:35:55+00:00
|
880bb9befb6c8258e8504cc8d8fbc4f115491652
|
# Dataset for project: csgo-weapon-classification
## Dataset Description
This dataset has for project csgo-weapon-classification was collected with the help of a bulk google image downloader.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1768x718 RGB PIL image>",
"target": 0
},
{
"image": "<716x375 RGBA PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['AK-47', 'AWP', 'Famas', 'Galil-AR', 'Glock', 'M4A1', 'M4A4', 'P-90', 'SG-553', 'UMP', 'USP'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1100 |
| valid | 275 |
|
Kaludi/data-csgo-weapon-classification
|
[
"task_categories:image-classification",
"region:us"
] |
2023-02-02T22:42:56+00:00
|
{"task_categories": ["image-classification"]}
|
2023-02-02T23:34:31+00:00
|
b9dbb115531962e828cc9c771a2f47ed5dbe55de
|
# MIRACL (en) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Loading the dataset
In [miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-en-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
|
Cohere/miracl-en-corpus-22-12
|
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-02-02T23:21:21+00:00
|
{"annotations_creators": ["expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["multilingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "tags": []}
|
2023-02-06T11:54:52+00:00
|
4eacd74558e17f4d5f8a34a637b6baf7dc71974c
|
sritang/hack
|
[
"region:us"
] |
2023-02-03T00:03:26+00:00
|
{}
|
2023-02-03T00:03:53+00:00
|
|
ebf20b2f1857068398e1e71b4e745d3c370e99c7
|
# Dataset for project: eurekaqa
This dataset has been trained for project eurekaQA.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"context": "Colquhoun's utilitarian approach to the problem \u2013 using a cost-benefit argument to obtain support from businesses standing to benefit \u2013 allowed him to achieve what Henry and John Fielding failed for their Bow Street detectives. Unlike the stipendiary system at Bow Street, the river police were full-time, salaried officers prohibited from taking private fees. His other contribution was the concept of preventive policing; his police were to act as a highly visible deterrent to crime by their permanent presence on the Thames. Colquhoun's innovations were a critical development leading up to Robert Peel's \"new\" police three decades later.",
"question": "How did the Thames River Police pay their employees?",
"answers.text": [
"full-time, salaried officers prohibited from taking private fees"
],
"answers.answer_start": [
295
]
},
{
"context": "The small woolen dolls called Maniae, hung on the Compitalia shrines, were thought a symbolic replacement for child-sacrifice to Mania, as Mother of the Lares. The Junii took credit for its abolition by their ancestor L. Junius Brutus, traditionally Rome's Republican founder and first consul. Political or military executions were sometimes conducted in such a way that they evoked human sacrifice, whether deliberately or in the perception of witnesses; Marcus Marius Gratidianus was a gruesome example.",
"question": "Who was Mania in Roman religion?",
"answers.text": [
"Mother of the Lares"
],
"answers.answer_start": [
139
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 8996 |
| valid | 998 |
|
Kaludi/data-eurekaQA
|
[
"language:en",
"region:us"
] |
2023-02-03T00:56:19+00:00
|
{"language": ["en"]}
|
2023-02-03T05:02:11+00:00
|
c3d1fb8cd067998b2e68b7557843d12cf4f80912
|
# Dataset Card for Crello
## Table of Contents
- [Dataset Card for Crello](#dataset-card-for-crello)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CanvasVAE github](https://github.com/CyberAgentAILab/canvas-vae)
- **Repository:**
- **Paper:** [CanvasVAE: Learning to Generate Vector Graphic Documents](https://arxiv.org/abs/2108.01249)
- **Leaderboard:**
- **Point of Contact:** [Kota Yamaguchi](https://github.com/kyamagu)
### Dataset Summary
The Crello dataset is compiled for the study of vector graphic documents. The dataset contains document meta-data such as canvas size and pre-rendered elements such as images or text boxes. The original templates were collected from [crello.com](https://crello.com) (now [create.vista.com](https://create.vista.com/)) and converted to a low-resolution format suitable for machine learning analysis.
### Usage
```python
import datasets
dataset = datasets.load_dataset("cyberagent/crello")
```
Old revision is available via `revision` option.
```python
import datasets
dataset = datasets.load_dataset("cyberagent/crello", revision="3.1")
```
### Supported Tasks and Leaderboards
[CanvasVAE](https://arxiv.org/abs/2108.01249) studies unsupervised document generation.
### Languages
Almost all design templates use English.
## Dataset Structure
### Data Instances
Each instance has scalar attributes (canvas) and sequence attributes (elements). Categorical values are stored as integer values. Check `ClassLabel` features of the dataset for the list of categorical labels.
```
{'id': '592d6c2c95a7a863ddcda140',
'length': 8,
'group': 4,
'format': 20,
'canvas_width': 3,
'canvas_height': 1,
'category': 0,
'title': 'Beauty Blog Ad Woman with Unusual Hairstyle',
'type': [1, 3, 3, 3, 3, 4, 4, 4],
'left': [0.0,
-0.0009259259095415473,
0.24444444477558136,
0.5712962746620178,
0.2657407522201538,
0.369228333234787,
0.2739444375038147,
0.44776931405067444],
'top': [0.0,
-0.0009259259095415473,
0.37037035822868347,
0.41296297311782837,
0.41296297311782837,
0.8946287035942078,
0.4549448788166046,
0.40591198205947876],
'width': [1.0,
1.0018517971038818,
0.510185182094574,
0.16296295821666718,
0.16296295821666718,
0.30000001192092896,
0.4990740716457367,
0.11388888955116272],
'height': [1.0,
1.0018517971038818,
0.25833332538604736,
0.004629629664123058,
0.004629629664123058,
0.016611294820904732,
0.12458471953868866,
0.02657807245850563],
'opacity': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'text': ['', '', '', '', '', 'STAY WITH US', 'FOLLOW', 'PRESS'],
'font': [0, 0, 0, 0, 0, 152, 172, 152],
'font_size': [0.0, 0.0, 0.0, 0.0, 0.0, 18.0, 135.0, 30.0],
'text_align': [0, 0, 0, 0, 0, 2, 2, 2],
'angle': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
'capitalize': [0, 0, 0, 0, 0, 0, 0, 0],
'line_height': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'letter_spacing': [0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 12.55813980102539, 3.0],
'suitability': [0],
'keywords': ['beautiful',
'beauty',
'blog',
'blogging',
'caucasian',
'cute',
'elegance',
'elegant',
'fashion',
'fashionable',
'femininity',
'glamour',
'hairstyle',
'luxury',
'model',
'stylish',
'vogue',
'website',
'woman',
'post',
'instagram',
'ig',
'insta',
'fashion',
'purple'],
'industries': [1, 8, 13],
'color': [[153.0, 118.0, 96.0],
[34.0, 23.0, 61.0],
[34.0, 23.0, 61.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0]],
'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>]}
```
To get a label for categorical values, use the `int2str` method:
```python
key = "font"
example = dataset[0]
dataset.features[key].int2str(example[key])
```
### Data Fields
In the following, categorical fields are shown as `categorical` type, but the actual storage is `int64`.
**Canvas attributes**
| Field | Type | Shape | Description |
| ------------- | ----------- | ------- | ----------------------------------------------------------------- |
| id | string | () | Template ID from crello.com |
| group | categorical | () | Broad design groups, such as social media posts or blog headers |
| format | categorical | () | Detailed design formats, such as Instagram post or postcard |
| category | categorical | () | Topic category of the design, such as holiday celebration |
| canvas_width | categorical | () | Canvas pixel width |
| canvas_height | categorical | () | Canvas pixel height |
| length | int64 | () | Length of elements |
| suitability | categorical | (None,) | List of display tags, only `mobile` tag exists |
| keywords | string | (None,) | List of keywords associated to this template |
| industries | categorical | (None,) | List of industry tags like `marketingAds` |
| preview | image | () | Preview image of the template for convenience; only for debugging |
| cluster_index | int64 | () | Cluster index used to split the dataset; only for debugging |
**Element attributes**
| Field | Type | Shape | Description |
| -------------- | ----------- | --------- | -------------------------------------------------------------------- |
| type | categorical | (None,) | Element type, such as vector shape, image, or text |
| left | float32 | (None,) | Element left position normalized to [0, 1] range w.r.t. canvas_width |
| top | float32 | (None,) | Element top position normalized to [0, 1] range w.r.t. canvas_height |
| width | float32 | (None,) | Element width normalized to [0, 1] range w.r.t. canvas_width |
| height | float32 | (None,) | Element height normalized to [0, 1] range w.r.t. canvas_height |
| color | int64 | (None, 3) | Extracted main RGB color of the element |
| opacity | float32 | (None,) | Opacity in [0, 1] range |
| image | image | (None,) | Pre-rendered 256x256 preview of the element encoded in PNG format |
| text | string | (None,) | Text content in UTF-8 encoding for text element |
| font | categorical | (None,) | Font family name for text element |
| font_size | float32 | (None,) | Font size (height) in pixels |
| text_align | categorical | (None,) | Horizontal text alignment, left, center, right for text element |
| angle | float32 | (None,) | Element rotation angle (radian) w.r.t. the center of the element |
| capitalize | categorical | (None,) | Binary flag to capitalize letters |
| line_height | float32 | (None,) | Scaling parameter to line height, default is 1.0 |
| letter_spacing | float32 | (None,) | Adjustment parameter for letter spacing, default is 0.0 |
Note that the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.
```
https://create.vista.com/artboard/?template=<template_id>
```
`left` and `top` can be negative because elements can be bigger than the canvas size.
### Data Splits
The Crello dataset has 3 splits: train, validation, and test. The current split is generated based on appearance-based clustering.
| Split | Count |
| --------- | ----- |
| train | 19095 |
| validaton | 1951 |
| test | 2375 |
### Visualization
Each example can be visualized in the following approach using [`skia-python`](https://kyamagu.github.io/skia-python/). Note the following does not guarantee a similar appearance to the original template. Currently, the quality of text rendering is far from perfect.
```python
import io
from typing import Any, Dict
import numpy as np
import skia
def render(features: datasets.Features, example: Dict[str, Any], max_size: float=512.) -> bytes:
"""Render parsed sequence example onto an image and return as PNG bytes."""
canvas_width = int(features["canvas_width"].int2str(example["canvas_width"]))
canvas_height = int(features["canvas_height"].int2str(example["canvas_height"]))
scale = min(1.0, max_size / canvas_width, max_size / canvas_height)
surface = skia.Surface(int(scale * canvas_width), int(scale * canvas_height))
with surface as canvas:
canvas.scale(scale, scale)
for index in range(example["length"]):
pil_image = example["image"][index]
image = skia.Image.frombytes(
pil_image.convert('RGBA').tobytes(),
pil_image.size,
skia.kRGBA_8888_ColorType)
left = example["left"][index] * canvas_width
top = example["top"][index] * canvas_height
width = example["width"][index] * canvas_width
height = example["height"][index] * canvas_height
rect = skia.Rect.MakeXYWH(left, top, width, height)
paint = skia.Paint(Alphaf=example["opacity"][index], AntiAlias=True)
angle = example["angle"][index]
with skia.AutoCanvasRestore(canvas):
if angle != 0:
degree = 180. * angle / np.pi
canvas.rotate(degree, left + width / 2., top + height / 2.)
canvas.drawImageRect(image, rect, paint=paint)
image = surface.makeImageSnapshot()
with io.BytesIO() as f:
image.save(f, skia.kPNG)
return f.getvalue()
```
## Dataset Creation
### Curation Rationale
The Crello dataset is compiled for the general study of vector graphic documents, with the goal of producing a dataset that offers complete vector graphic information suitable for neural methodologies.
### Source Data
#### Initial Data Collection and Normalization
The dataset is initially scraped from the former `crello.com` and pre-processed to the above format.
#### Who are the source language producers?
While [create.vista.com](https://create.vista.com/) owns those templates, the templates seem to be originally created by a specific group of design studios.
### Personal and Sensitive Information
The dataset does not contain any personal information about the creator but may contain a picture of people in the design template.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed for advancing the general study of vector graphic documents, especially for generative systems of graphic design. Successful utilization might enable the automation of creative workflow that human designers get involved in.
### Discussion of Biases
The templates contained in the dataset reflect the biases appearing in the source data, which could present gender biases in specific design categories.
### Other Known Limitations
Due to the unknown data specification of the source data, the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.
https://create.vista.com/artboard/?template=<template_id>
## Additional Information
### Dataset Curators
The Crello dataset was developed by [Kota Yamaguchi](https://github.com/kyamagu).
### Licensing Information
The origin of the dataset is [create.vista.com](https://create.vista.com) (formally, `crello.com`).
The distributor ("We") do not own the copyrights of the original design templates.
By using the Crello dataset, the user of this dataset ("You") must agree to the
[VistaCreate License Agreements](https://create.vista.com/faq/legal/licensing/license_agreements/).
The dataset is distributed under [CDLA-Permissive-2.0 license](https://cdla.dev/permissive-2-0/).
**Note**
We do not re-distribute the original files as we are not allowed by terms.
### Citation Information
@article{yamaguchi2021canvasvae,
title={CanvasVAE: Learning to Generate Vector Graphic Documents},
author={Yamaguchi, Kota},
journal={ICCV},
year={2021}
}
### Releases
4.0.0: v4 release (Dec 5, 2023)
- Change the dataset split based on the template appearance to avoid near-duplicates: no compatibility with v3.
- Class labels have been reordered: no compabilitity with v3.
- Small improvement to font rendering.
3.1: bugfix release (Feb 16, 2023)
- Fix a bug that ignores newline characters in some of the texts.
3.0: v3 release (Feb 13, 2023)
- Migrate to Hugging Face Hub.
- Fix various text rendering bugs.
- Change split generation criteria for avoiding near-duplicates: no compatibility with v2 splits.
- Incorporate a motion picture thumbnail in templates.
- Add `title`, `keywords`, `suitability`, and `industries` canvas attributes.
- Add `capitalize`, `line_height`, and `letter_spacing` element attributes.
2.0: v2 release (May 26, 2022)
- Add `text`, `font`, `font_size`, `text_align`, and `angle` element attributes.
- Include rendered text element in `image_bytes`.
1.0: v1 release (Aug 24, 2021)
### Contributions
Thanks to [@kyamagu](https://github.com/kyamagu) for adding this dataset.
|
cyberagent/crello
|
[
"task_categories:unconditional-image-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cdla-permissive-2.0",
"graphic design",
"design templates",
"arxiv:2108.01249",
"region:us"
] |
2023-02-03T01:31:45+00:00
|
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": "cdla-permissive-2.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["unconditional-image-generation"], "task_ids": [], "pretty_name": "crello", "tags": ["graphic design", "design templates"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "length", "dtype": "int64"}, {"name": "group", "dtype": {"class_label": {"names": {"0": "SM", "1": "HC", "2": "MM", "3": "SMA", "4": "EO", "5": "BG"}}}}, {"name": "format", "dtype": {"class_label": {"names": {"0": "Instagram Story", "1": "Instagram", "2": "Facebook", "3": "Facebook cover", "4": "Twitter", "5": "Facebook AD", "6": "Poster", "7": "Instagram AD", "8": "Tumblr", "9": "Image", "10": "Pinterest", "11": "Flayer", "12": "FB event cover", "13": "Postcard", "14": "Invitation", "15": "Youtube", "16": "Email header", "17": "Medium Rectangle", "18": "Poster US", "19": "Graphic", "20": "Large Rectangle", "21": "Card", "22": "Logo", "23": "Title", "24": "Skyscraper", "25": "Leaderboard", "26": "Presentation", "27": "Gift Certificate", "28": "VK Universal Post", "29": "Youtube Thumbnail", "30": "Business card", "31": "Book Cover", "32": "Presentation Wide", "33": "VK Community Cover", "34": "Certificate", "35": "Zoom Background", "36": "VK Post with Button", "37": "T-Shirt", "38": "Instagram Highlight Cover", "39": "Coupon", "40": "Letterhead", "41": "IGTV Cover", "42": "Schedule Planner", "43": "Album Cover", "44": "LinkedIn Cover", "45": "Storyboard", "46": "Recipe Card", "47": "Invoice", "48": "Resume", "49": "Menu", "50": "Mood Board", "51": "Mind Map", "52": "Label", "53": "Newsletter", "54": "Brochure", "55": "Ticket", "56": "Proposal", "57": "Snapchat Geofilter", "58": "Snapchat Moment Filter", "59": "Twitch Offline Banner", "60": "Twitch Profile Banner", "61": "Infographic", "62": "Mobile Presentation", "63": "Photo Book", "64": "Web Banner", "65": "Gallery Image", "66": "Calendar"}}}}, {"name": "canvas_width", "dtype": {"class_label": {"names": {"0": "1080", "1": "1200", "2": "940", "3": "851", "4": "360", "5": "1190", "6": "1920", "7": "419", "8": "1024", "9": "600", "10": "1600", "11": "735", "12": "595", "13": "3000", "14": "2560", "15": "1500", "16": "300", "17": "540", "18": "1296", "19": "336", "20": "500", "21": "432", "22": "560", "23": "160", "24": "1280", "25": "728", "26": "1000", "27": "241", "28": "1590", "29": "792", "30": "576", "31": "537", "32": "1008", "33": "420", "34": "1128", "35": "396", "36": "841", "37": "800", "38": "635", "39": "240", "40": "842"}}}}, {"name": "canvas_height", "dtype": {"class_label": {"names": {"0": "1080", "1": "1920", "2": "315", "3": "788", "4": "628", "5": "600", "6": "504", "7": "1683", "8": "298", "9": "500", "10": "512", "11": "1102", "12": "1440", "13": "200", "14": "400", "15": "250", "16": "810", "17": "1728", "18": "1200", "19": "280", "20": "841", "21": "288", "22": "90", "23": "1055", "24": "720", "25": "768", "26": "700", "27": "142", "28": "612", "29": "2560", "30": "2000", "31": "240", "32": "216", "33": "842", "34": "1296", "35": "2340", "36": "654", "37": "191", "38": "1600", "39": "297", "40": "595", "41": "480", "42": "576", "43": "320", "44": "380", "45": "141"}}}}, {"name": "category", "dtype": {"class_label": {"names": {"0": "holidaysCelebration", "1": "foodDrinks", "2": "fashionStyle", "3": "businessFinance", "4": "homeStuff", "5": "handcraftArt", "6": "beauty", "7": "leisureEntertainment", "8": "natureWildlife", "9": "educationScience", "10": "technology", "11": "medical", "12": "socialActivityCharity", "13": "realEstateBuilding", "14": "sportExtreme", "15": "travelsVacations", "16": "pets", "17": "religions", "18": "citiesPlaces", "19": "industry", "20": "transportation", "21": "kidsParents", "22": "all"}}}}, {"name": "title", "dtype": "string"}, {"name": "type", "sequence": {"class_label": {"names": {"0": "svgElement", "1": "textElement", "2": "imageElement", "3": "coloredBackground", "4": "maskElement"}}}}, {"name": "left", "sequence": "float32"}, {"name": "top", "sequence": "float32"}, {"name": "width", "sequence": "float32"}, {"name": "height", "sequence": "float32"}, {"name": "opacity", "sequence": "float32"}, {"name": "color", "sequence": {"sequence": "float32", "length": 3}}, {"name": "image", "sequence": "image"}, {"name": "text", "sequence": "string"}, {"name": "font", "sequence": {"class_label": {"names": {"0": "", "1": "Montserrat", "2": "Bebas Neue", "3": "Raleway", "4": "Josefin Sans", "5": "Cantarell", "6": "Playfair Display", "7": "Oswald", "8": "Blogger", "9": "Abril Fatface", "10": "Prompt", "11": "Comfortaa", "12": "Rubik", "13": "Open Sans", "14": "Roboto", "15": "Libre Baskerville", "16": "Quicksand", "17": "Dosis", "18": "Podkova", "19": "Lato", "20": "Cormorant Infant", "21": "Amatic Sc", "22": "Fjalla One", "23": "Playlist Script", "24": "Arapey", "25": "Baloo Tamma", "26": "Graduate", "27": "Titillium Web", "28": "Kreon", "29": "Nunito", "30": "Rammetto One", "31": "Anton", "32": "Poiret One", "33": "Alfa Slab One", "34": "Righteous", "35": "Play", "36": "Space Mono", "37": "Frank Ruhl Libre", "38": "Yanone Kaffeesatz", "39": "Pacifico", "40": "Bangers", "41": "Yellowtail", "42": "Droid Serif", "43": "Racing Sans One", "44": "Merriweather", "45": "Miriam Libre", "46": "Crete Round", "47": "Rubik One", "48": "Bungee", "49": "Sansita One", "50": "Patua One", "51": "Economica", "52": "Caveat", "53": "Philosopher", "54": "Limelight", "55": "Breathe", "56": "Rokkitt", "57": "Russo One", "58": "Noticia Text", "59": "Tinos", "60": "Oleo Script", "61": "Josefin Slab", "62": "Arima Madurai", "63": "Brusher Free Font", "64": "Old Standard Tt", "65": "Kalam", "66": "Patrick Hand", "67": "Playball", "68": "Six Caps", "69": "Bad Script", "70": "Orbitron", "71": "Contrail One", "72": "Selima Script", "73": "Gravitas One", "74": "El Messiri", "75": "Bubbler One", "76": "Italiana", "77": "Pompiere", "78": "Lemon Tuesday", "79": "Vast Shadow", "80": "Sunday", "81": "Cookie", "82": "Exo 2", "83": "Barrio", "84": "Radley", "85": "Mrs Sheppards", "86": "Grand Hotel", "87": "Great Vibes", "88": "Maven Pro", "89": "Knewave", "90": "Damion", "91": "Tulpen One", "92": "Parisienne", "93": "Superclarendon Regular", "94": "Oxygen", "95": "Nixie One", "96": "Permanent Marker", "97": "Medula One", "98": "Cabin Sketch", "99": "Vollkorn", "100": "Yeseva One", "101": "Montserrat Alternates", "102": "Satisfy", "103": "Sacramento", "104": "Carter One", "105": "Glass Antiqua", "106": "Mr Dafoe", "107": "Lauren", "108": "Oranienbaum", "109": "Scope One", "110": "Mr De Haviland", "111": "Pirou", "112": "Rise", "113": "Sensei", "114": "Yesteryear", "115": "Delius", "116": "Sue Ellen Francisco", "117": "Copse", "118": "Kaushan Script", "119": "Monda", "120": "Pattaya", "121": "Dancing Script", "122": "Reem Kufi", "123": "Playlist Caps", "124": "Beacon", "125": "Reenie Beanie", "126": "Overlock", "127": "Mrs Saint Delafield", "128": "Open Sans Condensed", "129": "Covered By Your Grace", "130": "Varela Round", "131": "Allura", "132": "Buda", "133": "Mikodacs", "134": "Arkana Script", "135": "Nothing You Could Do", "136": "Rochester", "137": "Fredericka The Great", "138": "Port Lligat Slab", "139": "Heebo", "140": "Arimo", "141": "Dawning Of A New Day", "142": "Aldrich", "143": "Neucha", "144": "Source Serif Pro", "145": "Shadows Into Light Two", "146": "Armata", "147": "Cutive Mono", "148": "Merienda One", "149": "Rissa Typeface", "150": "Stalemate", "151": "Assistant", "152": "Pathway Gothic One", "153": "Breathe Press", "154": "Suez One", "155": "Berkshire Swash", "156": "Rakkas", "157": "Pinyon Script", "158": "Pt Sans", "159": "Delius Swash Caps", "160": "Kurale", "161": "Offside", "162": "Clicker Script", "163": "Mate", "164": "Bentham", "165": "Rye", "166": "Lalezar", "167": "Julius Sans One", "168": "Quattrocento", "169": "V T323", "170": "Finger Paint", "171": "La Belle Aurore", "172": "Inconsolata", "173": "Press Start 2P", "174": "Junge", "175": "Iceberg", "176": "Kelly Slab", "177": "Handlee", "178": "Rosario", "179": "Gaegu", "180": "Homemade Apple", "181": "Londrina Shadow", "182": "Meddon", "183": "Elsie Swash Caps", "184": "Share Tech Mono", "185": "Black Ops One", "186": "Fauna One", "187": "Alice", "188": "Arizonia", "189": "Text Me One", "190": "Nova Square", "191": "Bungee Shade", "192": "Just Me Again Down Here", "193": "Jacques Francois Shadow", "194": "Cousine", "195": "Forum", "196": "Architects Daughter", "197": "Cedarville Cursive", "198": "Elsie", "199": "Sirin Stencil", "200": "Vampiro One", "201": "Dorsa", "202": "Marcellus Sc", "203": "Kumar One", "204": "Allerta Stencil", "205": "Courgette", "206": "Rationale", "207": "Gluk Znikomitno25", "208": "Happy Monkey", "209": "Stint Ultra Expanded", "210": "Rock Salt", "211": "Im Fell Dw Pica Sc", "212": "Faster One", "213": "Bellefair", "214": "Wire One", "215": "Geo", "216": "Farsan", "217": "League Script", "218": "Chathura", "219": "Euphoria Script", "220": "Zeyada", "221": "Jura", "222": "Loved By The King", "223": "Give You Glory", "224": "Znikomitno24", "225": "Gluk Glametrix", "226": "Alegreya Sans", "227": "Kristi", "228": "Knewave Outline", "229": "Pangolin", "230": "Okolaks", "231": "Seymour One", "232": "Didact Gothic", "233": "Kavivanar", "234": "Underdog", "235": "Alef", "236": "Italianno", "237": "Londrina Sketch", "238": "Secular One", "239": "Katibeh", "240": "Caesar Dressing", "241": "Lovers Quarrel", "242": "Iceland", "243": "Im Fell", "244": "Waiting For The Sunrise", "245": "David Libre", "246": "Marck Script", "247": "Kumar One Outline", "248": "Znikomit", "249": "Monsieur La Doulaise", "250": "Gruppo", "251": "Monofett", "252": "Gfs Didot", "253": "Petit Formal Script", "254": "Dukomdesign Constantine", "255": "Brusher", "256": "Eb Garamond", "257": "Ewert", "258": "Bilbo", "259": "Raleway Dots", "260": "Gabriela", "261": "Ruslan Display"}}}}, {"name": "font_size", "sequence": "float32"}, {"name": "text_align", "sequence": {"class_label": {"names": {"0": "", "1": "left", "2": "center", "3": "right"}}}}, {"name": "angle", "sequence": "float32"}, {"name": "capitalize", "sequence": {"class_label": {"names": {"0": "false", "1": "true"}}}}, {"name": "line_height", "sequence": "float32"}, {"name": "letter_spacing", "sequence": "float32"}, {"name": "suitability", "sequence": {"class_label": {"names": {"0": "mobile"}}}}, {"name": "keywords", "sequence": "string"}, {"name": "industries", "sequence": {"class_label": {"names": {"0": "marketingAds", "1": "entertainmentLeisure", "2": "services", "3": "retail", "4": "businessFinance", "5": "educationTraining", "6": "foodBeverages", "7": "artCrafts", "8": "fashionStyle", "9": "healthWellness", "10": "ecologyNature", "11": "nonProfitCharity", "12": "techGadgets", "13": "beautyCosmetics", "14": "homeLiving", "15": "familyKids", "16": "travelTourism", "17": "sportFitness", "18": "corporate", "19": "petsAnimals", "20": "realEstateConstruction", "21": "transportDelivery", "22": "religionFaith", "23": "hrRecruitment"}}}}, {"name": "preview", "dtype": "image"}, {"name": "cluster_index", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 5058614277.34, "num_examples": 19095}, {"name": "validation", "num_bytes": 538185754.149, "num_examples": 1951}, {"name": "test", "num_bytes": 649876234.375, "num_examples": 2375}], "download_size": 6188050025, "dataset_size": 6246676265.864}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-12-05T14:07:33+00:00
|
b9bbfff396d746570663f91ae5732c71f353eb22
|
# Dataset for project: food-category-classification
## Dataset Description
This dataset is for project food-category-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<512x512 RGB PIL image>",
"target": 0
},
{
"image": "<512x512 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Bread', 'Dairy product', 'Dessert', 'Egg', 'Fried food', 'Meat', 'Noodles-Pasta', 'Rice', 'Seafood', 'Soup', 'Vegetable-Fruit'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1210 |
| valid | 275 |
|
Kaludi/data-food-category-classification
|
[
"task_categories:image-classification",
"region:us"
] |
2023-02-03T01:48:48+00:00
|
{"task_categories": ["image-classification"]}
|
2023-02-03T02:09:07+00:00
|
0f8fdb357e569e73a2326a3cdd2b7902da491c8c
|
# Dataset Card for "clevr-with-depth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
erkam/clevr-with-depth
|
[
"region:us"
] |
2023-02-03T02:09:11+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "depth", "dtype": "image"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 115079852.0, "num_examples": 1400}, {"name": "test", "num_bytes": 24726160.0, "num_examples": 300}, {"name": "val", "num_bytes": 24696560.0, "num_examples": 300}], "download_size": 164000762, "dataset_size": 164502572.0}}
|
2023-02-03T02:09:24+00:00
|
0523de78ca34c417012823258250debe49b58b39
|
# MIRACL (en) embedded with cohere.ai `multilingual-22-12` encoder
We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
The query embeddings can be found in [Cohere/miracl-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-corpus-22-12).
For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
Dataset info:
> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
>
> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Loading the dataset
In [miracl-en-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-en-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train", streaming=True)
for doc in docs:
docid = doc['docid']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
Have a look at [miracl-en-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
To search in the documents, you must use **dot-product**.
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
A full search example:
```python
# Attention! For large datasets, this requires a lot of memory to store
# all document embeddings and to compute the dot product scores.
# Only use this for smaller datasets. For large datasets, use a vector DB
from datasets import load_dataset
import torch
#Load documents + embeddings
docs = load_dataset(f"Cohere/miracl-en-corpus-22-12", split="train")
doc_embeddings = torch.tensor(docs['emb'])
# Load queries
queries = load_dataset(f"Cohere/miracl-en-queries-22-12", split="dev")
# Select the first query as example
qid = 0
query = queries[qid]
query_embedding = torch.tensor(queries['emb'])
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query['query'])
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
```
You can get embeddings for new queries using our API:
```python
#Run: pip install cohere
import cohere
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
texts = ['my search query']
response = co.embed(texts=texts, model='multilingual-22-12')
query_embedding = response.embeddings[0] # Get the embedding for the first text
```
## Performance
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
|---|---|---|---|---|
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
Further languages (not supported by Elasticsearch):
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|---|---|---|
| miracl-fa | 44.8 | 53.6 |
| miracl-ja | 49.0 | 61.0 |
| miracl-ko | 50.9 | 64.8 |
| miracl-sw | 61.4 | 74.5 |
| miracl-te | 67.8 | 72.3 |
| miracl-th | 60.2 | 71.9 |
| miracl-yo | 56.4 | 62.2 |
| miracl-zh | 43.8 | 56.5 |
| **Avg** | 54.3 | 64.6 |
|
Cohere/miracl-en-queries-22-12
|
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:expert-generated",
"multilinguality:multilingual",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-02-03T02:21:53+00:00
|
{"annotations_creators": ["expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["multilingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text-retrieval"], "task_ids": ["document-retrieval"], "tags": []}
|
2023-02-06T11:54:43+00:00
|
1debd8e6fd510405990950c4f19881228499844b
|
# Dataset Card for "Sentences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nlpproject2023/Sentences
|
[
"region:us"
] |
2023-02-03T02:36:08+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "facts", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 3896905, "num_examples": 7405}], "download_size": 2542119, "dataset_size": 3896905}}
|
2023-02-03T02:36:19+00:00
|
d52524276c164ae52db5b41751d2794f85b46f56
|
pengGG/FVVCBFDDF
|
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] |
2023-02-03T02:41:00+00:00
|
{"license": "bigscience-bloom-rail-1.0"}
|
2023-02-03T02:41:00+00:00
|
|
ccdd19b674df992f1cbe72d1d38b34a290deae17
|
# Dataset Card for "OxfordPets_test_facebook_opt_350m_Attributes_Caption_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/OxfordPets_test_facebook_opt_350m_Attributes_Caption_ns_3669_random
|
[
"region:us"
] |
2023-02-03T02:43:55+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 122169585.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 124212797.375, "num_examples": 3669}], "download_size": 241370285, "dataset_size": 246382382.75}}
|
2023-02-03T02:48:56+00:00
|
d05d1b6b0c2471ac161ba49eeee2064eb52529a7
|
# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Attributes_Caption_ns_5647_random
|
[
"region:us"
] |
2023-02-03T03:29:01+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 85893233.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 88896569.125, "num_examples": 5647}], "download_size": 168043271, "dataset_size": 174789802.25}}
|
2023-02-03T03:50:07+00:00
|
d4b231a6d20a2bc8770131da8110e99803ec6260
|
# Dataset Card for "Caltech101_not_background_test_facebook_opt_1.3b_Attributes_Caption_ns_5647_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_1.3b_Attributes_Caption_ns_5647_random
|
[
"region:us"
] |
2023-02-03T03:40:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 85893289.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 88897029.125, "num_examples": 5647}], "download_size": 168050181, "dataset_size": 174790318.25}}
|
2023-02-03T04:10:27+00:00
|
24d74d538a3c96c0a1a40d04a33e4b32d3fa9687
|
# Dataset Card for "DTD_parition1_test_facebook_opt_350m_Attributes_Caption_ns_1880_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/DTD_parition1_test_facebook_opt_350m_Attributes_Caption_ns_1880_random
|
[
"region:us"
] |
2023-02-03T04:39:26+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 92259986.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93272910.0, "num_examples": 1880}], "download_size": 91287158, "dataset_size": 185532896.0}}
|
2023-02-03T05:22:00+00:00
|
9a2dede5c45876165fff23537e7436ce39a7ad30
|
# Dataset Card for "DTD_parition1_test_facebook_opt_1.3b_Attributes_Caption_ns_1880_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/DTD_parition1_test_facebook_opt_1.3b_Attributes_Caption_ns_1880_random
|
[
"region:us"
] |
2023-02-03T04:42:51+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 92259967.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93272711.0, "num_examples": 1880}], "download_size": 91287196, "dataset_size": 185532678.0}}
|
2023-02-03T05:26:50+00:00
|
1ea9c65e205a05ce25a5db660018f00e1b533f0c
|
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Attributes_Caption_ns_1880_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/DTD_parition1_test_facebook_opt_2.7b_Attributes_Caption_ns_1880_random
|
[
"region:us"
] |
2023-02-03T04:47:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 92259942.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93272325.0, "num_examples": 1880}], "download_size": 91288071, "dataset_size": 185532267.0}}
|
2023-02-03T05:33:26+00:00
|
dbe1d4b6ba8b65c19884650400892153d36c2e68
|
# Dataset Card for "DTD_parition1_test_facebook_opt_350m_Visclues_ns_1880_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/DTD_parition1_test_facebook_opt_350m_Visclues_ns_1880_random
|
[
"region:us"
] |
2023-02-03T05:10:10+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 92562811.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93877903.0, "num_examples": 1880}], "download_size": 182696811, "dataset_size": 186440714.0}}
|
2023-02-03T05:36:44+00:00
|
3e109fe74311ab2301a9c60f9309edaad4d4636a
|
# Dataset Card for "DTD_parition1_test_facebook_opt_1.3b_Visclues_ns_1880_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/DTD_parition1_test_facebook_opt_1.3b_Visclues_ns_1880_random
|
[
"region:us"
] |
2023-02-03T05:13:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 92562773.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93877652.0, "num_examples": 1880}], "download_size": 182697216, "dataset_size": 186440425.0}}
|
2023-02-03T05:42:04+00:00
|
4268a130d108755b5a49ea857d71bf1ae2e9f062
|
# Dataset Card for "Sound_Spectrogram"
# Questions about dataset
1. What is Spectrogram?
2. How were these converted to image?
3. Where is dataset from?
4. How can I use this?
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jha2ee/Sound_Spectrogram
|
[
"task_categories:feature-extraction",
"sound",
"environment",
"instrument",
"effect",
"region:us"
] |
2023-02-03T05:15:18+00:00
|
{"task_categories": ["feature-extraction"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Sound_Drum", "1": "Sound_Piano", "2": "Sound_Violin", "3": "airplane", "4": "breathing", "5": "brushing_teeth", "6": "can_opening", "7": "car_horn", "8": "cat", "9": "chainsaw", "10": "chirping_birds", "11": "church_bells", "12": "clapping", "13": "clock_alarm", "14": "clock_tick", "15": "coughing", "16": "cow", "17": "crackling_fire", "18": "crickets", "19": "crow", "20": "crying_baby", "21": "dog", "22": "door_wood_creaks", "23": "door_wood_knock", "24": "drinking_sipping", "25": "engine", "26": "fireworks", "27": "footsteps", "28": "frog", "29": "glass_breaking", "30": "helicopter", "31": "hen", "32": "insects", "33": "keyboard_typing", "34": "laughing", "35": "mouse_click", "36": "pig", "37": "pouring_water", "38": "rain", "39": "rooster", "40": "sea_waves", "41": "sheep", "42": "siren", "43": "sneezing", "44": "snoring", "45": "toilet_flush", "46": "train", "47": "vacuum_cleaner", "48": "washing_machine", "49": "water_drops", "50": "wind"}}}}], "splits": [{"name": "train", "num_bytes": 141766644.635, "num_examples": 1981}], "download_size": 141547931, "dataset_size": 141766644.635}, "tags": ["sound", "environment", "instrument", "effect"]}
|
2023-02-08T00:40:19+00:00
|
1e51be5d1685b5a45dd4c2a94da9e1a2210b4166
|
# Dataset Card for "DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/DTD_parition1_test_facebook_opt_2.7b_Visclues_ns_1880_random
|
[
"region:us"
] |
2023-02-03T05:18:45+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 92562783.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93877509.0, "num_examples": 1880}], "download_size": 182697658, "dataset_size": 186440292.0}}
|
2023-02-03T05:49:55+00:00
|
667dcb591155dbd5e5f8d1f4a29222b3cecd3dcc
|
# Dataset Card for "ms_marco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nschantz21/ms_marco
|
[
"region:us"
] |
2023-02-03T05:22:53+00:00
|
{"dataset_info": {"features": [{"name": "pid", "dtype": "int64"}, {"name": "passage", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3089204431, "num_examples": 8841823}], "download_size": 1688705276, "dataset_size": 3089204431}}
|
2023-02-03T05:25:50+00:00
|
e265b62f8818b8a4079e50dc4aa17dc2ee68990a
|
yizhang7210/curated_ms_marco
|
[
"license:mit",
"region:us"
] |
2023-02-03T06:51:50+00:00
|
{"license": "mit"}
|
2023-02-05T18:20:30+00:00
|
|
00ad66e9998060d686d0310f114887ae23b15429
|
# Dataset Card for "pavel-github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pavel-nesterov/pavel-github-issues
|
[
"region:us"
] |
2023-02-03T07:23:54+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "dtype": "string"}, {"name": "assignees", "dtype": "string"}, {"name": "milestone", "dtype": "string"}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "updated_at", "dtype": "string"}, {"name": "closed_at", "dtype": "string"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "float64"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "dtype": "string"}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "float64"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "dtype": "string"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 24151143, "num_examples": 5498}], "download_size": 5994366, "dataset_size": 24151143}}
|
2023-02-03T07:24:01+00:00
|
617d3dcb7cf617034e5fff5e2a34152daa23bb8f
|
deeptigp/car_generation_diffusion
|
[
"license:unknown",
"region:us"
] |
2023-02-03T08:26:05+00:00
|
{"license": "unknown"}
|
2023-02-03T09:10:00+00:00
|
|
b386012c9a47a916a59c24489bdc15444dbc0b28
|
# Dataset Card for "emea_en-de_20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
muibk/emea_en-de_20k
|
[
"region:us"
] |
2023-02-03T08:45:42+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "translation", "struct": [{"name": "de", "dtype": "string"}, {"name": "en", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2746077.5302918116, "num_examples": 18000}, {"name": "test", "num_bytes": 152559.8627939895, "num_examples": 1000}, {"name": "valid", "num_bytes": 152559.8627939895, "num_examples": 1000}], "download_size": 1901357, "dataset_size": 3051197.2558797905}}
|
2023-02-03T08:45:58+00:00
|
f0b738ffc26ec622cd3b7002950a46392cf07b55
|
TheLastBen/PPS
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-02-03T09:21:27+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-11-24T10:02:08+00:00
|
|
0bffaf4a3ffbf0c7ca325adab756bb1c535a2ac2
|
huggingface-projects/filter-bad-models
|
[
"license:mit",
"region:us"
] |
2023-02-03T10:04:19+00:00
|
{"license": "mit"}
|
2023-04-17T11:32:31+00:00
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.