id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
alexrs/alpaca-cleaned-10-clusters | alexrs | 2023-10-16T14:36:58Z | 155 | 0 | null | [
"region:us"
] | 2023-10-16T14:36:58Z | 2023-10-16T14:36:55.000Z | 2023-10-16T14:36:55 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
- name: cluster
dtype: int32
splits:
- name: train
num_bytes: 40490946
num_examples: 51760
download_size: 24184864
dataset_size: 40490946
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "alpaca-cleaned-10-clusters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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CShorten/KerasBERT | CShorten | 2022-06-28T11:51:07Z | 154 | 2 | null | [
"region:us"
] | 2022-06-28T11:51:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | <h1>KerasBERT</h1>
<ul>
<li>All Data</li>
<li>Keras API Docs</li>
<li>Keras Developer Guides</li>
<li>Keras Code Examples</li>
</ul>
Please cite KerasBERT: Modeling the Keras Language, Connor Shorten and Taghi M. Khoshgoftaar. https://ieeexplore.ieee.org/abstract/document/9679980. | [
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GalacticAI/Noirset | GalacticAI | 2021-06-30T00:59:28Z | 154 | 0 | null | [
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] | 2021-06-30T00:59:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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GeoffVdr/cv8_trainval_processed | GeoffVdr | 2022-01-31T15:10:08Z | 154 | 0 | null | [
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] | 2022-01-31T15:10:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Halilyesilceng/autonlp-data-nameEntityRecognition | Halilyesilceng | 2021-03-30T23:41:25Z | 154 | 0 | null | [
"region:us"
] | 2021-03-30T23:41:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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HarleyQ/WitcherDialogue | HarleyQ | 2021-06-02T16:15:44Z | 154 | 0 | null | [
"region:us"
] | 2021-06-02T16:15:44Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Harveenchadha/bol-models | Harveenchadha | 2021-09-17T05:51:52Z | 154 | 0 | null | [
"region:us"
] | 2021-09-17T05:51:52Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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HenryAI/KerasDeveloperGuides.txt | HenryAI | 2021-12-15T15:56:47Z | 154 | 0 | null | [
"region:us"
] | 2021-12-15T15:56:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Keras developer guides from https://keras.io/guides/ <br />
Formatted for input to: https://huggingface.co/blog/how-to-train | [
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Husain/intent-classification-en-fr | Husain | 2021-07-28T13:06:35Z | 154 | 0 | null | [
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Iftoo95/Arabic_Sentiment_and_Topics | Iftoo95 | 2021-11-20T14:50:45Z | 154 | 0 | null | [
"region:us"
] | 2021-11-20T14:50:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Arabic Twitter based dataset with multi-labels that contains two classes:
1. Sentiment class: classifies tweets as Positive, Negative and Neutral
2. Topic class: Classifies tweets as Politics, Business and Health | [
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JIWON/nil_dataset | JIWON | 2022-02-07T00:34:03Z | 154 | 0 | null | [
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] | 2022-02-07T00:34:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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LysandreJik/stargazers | LysandreJik | 2021-09-26T22:47:03Z | 154 | 0 | null | [
"region:us"
] | 2021-09-26T22:47:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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LysandreJik/test-16344347220590 | LysandreJik | 2021-10-17T01:38:42Z | 154 | 0 | null | [
"region:us"
] | 2021-10-17T01:38:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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LysandreJik/test-16344347234752 | LysandreJik | 2021-10-17T01:38:43Z | 154 | 0 | null | [
"region:us"
] | 2021-10-17T01:38:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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LysandreJik/test-16344351925697 | LysandreJik | 2021-10-17T01:46:34Z | 154 | 0 | null | [
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] | 2021-10-17T01:46:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Linda/test1111 | Linda | 2021-11-11T07:08:48Z | 154 | 0 | null | [
"region:us"
] | 2021-11-11T07:08:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | dataset card | [
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Lumos/yahoo_hga | Lumos | 2021-12-30T11:06:07Z | 154 | 0 | null | [
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MBAH/MOVIESON | MBAH | 2021-09-18T00:04:34Z | 154 | 0 | null | [
"region:us"
] | 2021-09-18T00:04:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | https://mahoningmed.org/docs/123movies-watch-after-we-fell-2021-hd-full-movie-online-free/
https://mahoningmed.org/docs/123movies-watch-cinderella-2021-hd-full-movie-online-free/
https://mahoningmed.org/docs/123movies-watch-cryptozoo-2021full-movie-hd-free/
https://mahoningmed.org/docs/123movieswatch-breathless-2021hd-full-movie-online/
https://mahoningmed.org/docs/123movieswatch-firebird-2021full-hd-movie-online-free/
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https://mahoningmed.org/docs/123movies-watch-hes-all-that-2021-hd-full-movie-online-free/
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https://mahoningmed.org/docs/123movies-watch-jurassic-hunt-2021-hd-full-movie-online-free-2/
https://mahoningmed.org/docs/123movies-watch-vacation-friends-2021-hd-full-movie-online-free/
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https://mahoningmed.org/docs/watch-after-we-fell-2021full-hd-movie-online-free/
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0.13240161538124... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MarianaSahagun/test | MarianaSahagun | 2021-03-26T18:58:13Z | 154 | 0 | null | [
"region:us"
] | 2021-03-26T18:58:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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MarkusDressel/cord | MarkusDressel | 2021-12-02T10:33:43Z | 154 | 0 | null | [
"region:us"
] | 2021-12-02T10:33:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Marzipan/QA4PC | Marzipan | 2021-11-16T13:45:34Z | 154 | 0 | null | [
"region:us"
] | 2021-11-16T13:45:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ## QA4PC Dataset (paper: Cross-Policy Compliance Detection via Question Answering)
### Train Sets
To create training set or entailment and QA tasks, download the ShARC data from here: https://sharc-data.github.io/data.html. After that, run the script from _create_train_from_sharc.py_, by providing the path to the ShARC train and development sets.
### Evaluation Sets
#### Entailment Data
The following files contain the data for the entailment task. This includes the policy + questions, a scenario and an answer (_Yes, No, Maybe_). Each datapoint also contain the information from the ShARC dataset such as tree_id and source_url.
- __dev_entailment_qa4pc.json__
- __test_entailment_qa4pc.json__
#### QA Data
The following files contain the data for the QA task.
- __dev_sc_qa4pc.json__
- __test_sc_qa4pc.json__
The following file contains the expression tree data for the dev and test sets. Each tree includes a policy, a set of questions and a logical expression.
- __trees_dev_test_qa4pc.json__
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0.09866984188556671... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Mrleo1nid/Test_ru_dataset | Mrleo1nid | 2021-09-27T02:19:08Z | 154 | 0 | null | [
"region:us"
] | 2021-09-27T02:19:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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NLPC-UOM/Sinhala-Stopword-list | NLPC-UOM | 2022-10-25T10:02:05Z | 154 | 0 | null | [
"language:si",
"license:mit",
"region:us"
] | 2022-10-25T10:02:05Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language:
- si
license:
- mit
---
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Nathanael/NPS | Nathanael | 2021-12-17T15:14:19Z | 154 | 0 | null | [
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Navigator/dodydard-marty | Navigator | 2022-02-16T11:52:22Z | 154 | 0 | null | [
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Niciu/github-issues | Niciu | 2022-03-01T13:52:02Z | 154 | 0 | null | [
"region:us"
] | 2022-03-01T13:52:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | restsfds | [
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NikolajW/NPS_nonNormalized-Cased | NikolajW | 2021-11-05T12:20:30Z | 154 | 0 | null | [
"region:us"
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Ofrit/tmp | Ofrit | 2021-02-25T11:42:23Z | 154 | 0 | null | [
"region:us"
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PDJ107/riot-data | PDJ107 | 2021-12-20T19:11:17Z | 154 | 0 | null | [
"region:us"
] | 2021-12-20T19:11:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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PaulLerner/viquae_all_images | PaulLerner | 2022-03-10T10:59:10Z | 154 | 0 | null | [
"region:us"
] | 2022-03-10T10:59:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | See https://github.com/PaulLerner/ViQuAE
---
license: cc-by-4.0
---
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PaulLerner/viquae_images | PaulLerner | 2022-02-15T17:29:53Z | 154 | 0 | null | [
"region:us"
] | 2022-02-15T17:29:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | See https://github.com/PaulLerner/ViQuAE
---
license: cc-by-4.0
---
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Pengfei/test | Pengfei | 2021-09-17T14:07:13Z | 154 | 0 | null | [
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Pengfei/test1 | Pengfei | 2021-11-05T00:46:07Z | 154 | 0 | null | [
"region:us"
] | 2021-11-05T00:46:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This is the dataset | [
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Pongsaky/Wiki_SCG | Pongsaky | 2021-07-24T22:12:47Z | 154 | 0 | null | [
"region:us"
] | 2021-07-24T22:12:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Pyjay/emotion_nl | Pyjay | 2022-02-14T23:26:59Z | 154 | 0 | null | [
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] | 2022-02-14T23:26:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Pyke/patent_abstract | Pyke | 2021-08-06T05:13:33Z | 154 | 0 | null | [
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] | 2021-08-06T05:13:33Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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QA/abk-eng | QA | 2021-03-09T19:12:22Z | 154 | 0 | null | [
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RBG-AI/CoRePooL | RBG-AI | 2021-11-14T09:33:21Z | 154 | 0 | null | [
"region:us"
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Recognai/gutenberg_spacy-ner | Recognai | 2022-02-17T12:37:06Z | 154 | 0 | null | [
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RuudVelo/commonvoice_mt_8_processed | RuudVelo | 2022-02-05T19:46:53Z | 154 | 0 | null | [
"region:us"
] | 2022-02-05T19:46:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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RuudVelo/commonvoice_nl_8_processed | RuudVelo | 2022-02-04T19:54:25Z | 154 | 0 | null | [
"region:us"
] | 2022-02-04T19:54:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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SaulLu/test | SaulLu | 2021-08-23T12:39:00Z | 154 | 0 | null | [
"region:us"
] | 2021-08-23T12:39:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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SergeiGKS/wikiner_fr_job | SergeiGKS | 2021-12-14T09:48:59Z | 154 | 0 | null | [
"region:us"
] | 2021-12-14T09:48:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Wikidepia/IndoSQuAD | Wikidepia | 2021-03-29T06:55:14Z | 154 | 0 | null | [
"region:us"
] | 2021-03-29T06:55:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
florianbussmann/train_tickets-yu2020pick | florianbussmann | 2022-01-19T14:18:09Z | 154 | 0 | null | [
"region:us"
] | 2022-01-19T14:18:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622264862060547,
0.43461528420448303,
-0.52829909324646,
0.7012971639633179,
0.7915720343589783,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104477167129517,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
LDJnr/LessWrong-Amplify-Instruct | LDJnr | 2023-11-21T17:55:39Z | 154 | 27 | null | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"Physics",
"Biology",
"Math",
"Chemistry",
"Culture",
"Logic",
"region:us"
] | 2023-11-21T17:55:39Z | 2023-09-26T01:42:29.000Z | 2023-09-26T01:42:29 | ---
license: apache-2.0
task_categories:
- conversational
- question-answering
- text-generation
language:
- en
tags:
- Physics
- Biology
- Math
- Chemistry
- Culture
- Logic
pretty_name: LessWrong-Amplify-Instruct
size_categories:
- n<1K
---
## This is the Official LessWrong-Amplify-Instruct dataset. Over 500 multi-turn examples, and many more coming soon!
- This leverages Amplify-Instruct method to extend thousands of scraped Less-Wrong posts into advanced in-depth multi-turn conversations.
- Comprised of over 500 highly filtered multi-turn conversations between GPT-4 and real humans.
- Average context length per conversation is over 2,000 tokens. (will measure this more accurately soon)
- Synthetically created using a newly developed pipeline that leverages GPT-4 to dynamically role play and inquire as the human and assistant.
- Each conversation is optimized to amplify the raw knowledge retreival of the model and delve deep into obscure and advanced topics.
## Purpose?
- This dataset is not intended to be trained on by itself, however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such!
## Quality filtering and cleaning.
- Extensive cleaning was done to filter out instances of overt AI moralizing or related behaviour, such as "As an AI language model" and "September 2021"
## Credits
During the curation process, there can be some relatively arduos steps when it comes to actually executing on the best experimentation or concepts for how to filter examples out.
Luckily there is folks over at NousResearch that helped expedite this process with little to no sacrifices in quality, big thank you to J-Supha specifically for making these types of significant contributions.
## Future Plans & How you can help!
This is a relatively early build amongst the grand plans for the future of what I plan to work on!
In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets.
If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!
Citation:
```
@article{daniele2023amplify-instruct,
title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.},
author={Daniele, Luigi and Suphavadeeprasit},
journal={arXiv preprint arXiv:(comming soon)},
year={2023}
}
``` | [
-0.626685380935669,
-0.9070243835449219,
0.35924187302589417,
0.08426865190267563,
-0.19064554572105408,
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0.4846520721912384,
-0.8370725512504578,
-0.3607790768146515,
-0.40499505400657654,
0.0810349658131... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
vlsp-2023-vllm/mmlu | vlsp-2023-vllm | 2023-09-30T03:37:34Z | 154 | 0 | null | [
"region:us"
] | 2023-09-30T03:37:34Z | 2023-09-29T19:08:22.000Z | 2023-09-29T19:08:22 | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: answer
dtype: int64
- name: question
dtype: string
- name: choices
sequence: string
splits:
- name: validation
num_bytes: 890402
num_examples: 1456
- name: dev
num_bytes: 140819
num_examples: 271
- name: test
num_bytes: 7615124
num_examples: 13062
download_size: 4415183
dataset_size: 8646345
---
References: https://huggingface.co/datasets/cais/mmlu
# MMLU (Vietnamese translation version)
## Install
To install `lm-eval` from the github repository main branch, run:
```bash
git clone https://github.com/hieunguyen1053/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```
## Basic Usage
> **Note**: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.
### Hugging Face `transformers`
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. vlsp-2023-vllm/hoa-1b4) on `mmlu` you can use the following command:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4 \
--tasks mmlu_vi \
--device cuda:0
```
Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4,revision=step100000,dtype="float" \
--tasks mmlu_vi \
--device cuda:0
```
To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you instead use `hf-seq2seq`. *To evaluate (causal) models across multiple GPUs, use `--model hf-causal-experimental`*
> **Warning**: Choosing the wrong model may result in erroneous outputs despite not erroring. | [
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-0.8083663582801819,
0.535996675491333,
0.4472784101963043,
-0.032260749489068985,
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0.028149960562586784,
-0.18562625348567963,
0.23326325416564941,
0.3332287669181824,
-0.6785079836845398,
-0.272232323884964,
-0.5910865664482117,
0.1666639000177383... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
swj0419/BookMIA | swj0419 | 2023-11-03T16:39:56Z | 154 | 0 | null | [
"license:mit",
"arxiv:2310.16789",
"region:us"
] | 2023-11-03T16:39:56Z | 2023-11-03T07:54:57.000Z | 2023-11-03T07:54:57 | ---
license: mit
---
# 📘 BookMIA Datasets
The **BookMIA datasets** serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003).
The dataset contains non-member and member data:
- non-member data consists of text excerpts from books first published in 2023
- member data includes text excerpts from older books, as categorized by Chang et al. in 2023.
### 📌 Applicability
The datasets can be applied to various OpenAI models released before **2023**:
- text-davinci-001
- text-davinci-002
- ... and more.
## Loading the datasets
To load the dataset:
```python
from datasets import load_dataset
dataset = load_dataset("swj0419/BookMIA")
```
* Text Lengths: `512`.
* *Label 0*: Refers to the unseen data during pretraining. *Label 1*: Refers to the seen data.
## 🛠️ Codebase
For evaluating MIA methods on our datasets, visit our [GitHub repository](https://github.com/swj0419/detect-pretrain-code).
## ⭐ Citing our Work
If you find our codebase and datasets beneficial, kindly cite our work:
```bibtex
@misc{shi2023detecting,
title={Detecting Pretraining Data from Large Language Models},
author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer},
year={2023},
eprint={2310.16789},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
[1] Kent K Chang, Mackenzie Cramer, Sandeep Soni, and David Bamman. Speak, memory: An archaeology of books known to chatgpt/gpt-4. arXiv preprint arXiv:2305.00118, 2023. | [
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0.12174297869205475,
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0.04635172709822655,
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0.36789223551750183,
-0.724067211151123,
-0.6199515461921692,
-0.4439494013786316,
0.03870987147092... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
danjacobellis/imagenet_dino | danjacobellis | 2023-11-22T15:05:57Z | 154 | 0 | null | [
"region:us"
] | 2023-11-22T15:05:57Z | 2023-11-20T18:37:49.000Z | 2023-11-20T18:37:49 | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': tench, Tinca tinca
'1': goldfish, Carassius auratus
'2': great white shark, white shark, man-eater, man-eating shark, Carcharodon
carcharias
'3': tiger shark, Galeocerdo cuvieri
'4': hammerhead, hammerhead shark
'5': electric ray, crampfish, numbfish, torpedo
'6': stingray
'7': cock
'8': hen
'9': ostrich, Struthio camelus
'10': brambling, Fringilla montifringilla
'11': goldfinch, Carduelis carduelis
'12': house finch, linnet, Carpodacus mexicanus
'13': junco, snowbird
'14': indigo bunting, indigo finch, indigo bird, Passerina cyanea
'15': robin, American robin, Turdus migratorius
'16': bulbul
'17': jay
'18': magpie
'19': chickadee
'20': water ouzel, dipper
'21': kite
'22': bald eagle, American eagle, Haliaeetus leucocephalus
'23': vulture
'24': great grey owl, great gray owl, Strix nebulosa
'25': European fire salamander, Salamandra salamandra
'26': common newt, Triturus vulgaris
'27': eft
'28': spotted salamander, Ambystoma maculatum
'29': axolotl, mud puppy, Ambystoma mexicanum
'30': bullfrog, Rana catesbeiana
'31': tree frog, tree-frog
'32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
'33': loggerhead, loggerhead turtle, Caretta caretta
'34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
'35': mud turtle
'36': terrapin
'37': box turtle, box tortoise
'38': banded gecko
'39': common iguana, iguana, Iguana iguana
'40': American chameleon, anole, Anolis carolinensis
'41': whiptail, whiptail lizard
'42': agama
'43': frilled lizard, Chlamydosaurus kingi
'44': alligator lizard
'45': Gila monster, Heloderma suspectum
'46': green lizard, Lacerta viridis
'47': African chameleon, Chamaeleo chamaeleon
'48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus
komodoensis
'49': African crocodile, Nile crocodile, Crocodylus niloticus
'50': American alligator, Alligator mississipiensis
'51': triceratops
'52': thunder snake, worm snake, Carphophis amoenus
'53': ringneck snake, ring-necked snake, ring snake
'54': hognose snake, puff adder, sand viper
'55': green snake, grass snake
'56': king snake, kingsnake
'57': garter snake, grass snake
'58': water snake
'59': vine snake
'60': night snake, Hypsiglena torquata
'61': boa constrictor, Constrictor constrictor
'62': rock python, rock snake, Python sebae
'63': Indian cobra, Naja naja
'64': green mamba
'65': sea snake
'66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
'67': diamondback, diamondback rattlesnake, Crotalus adamanteus
'68': sidewinder, horned rattlesnake, Crotalus cerastes
'69': trilobite
'70': harvestman, daddy longlegs, Phalangium opilio
'71': scorpion
'72': black and gold garden spider, Argiope aurantia
'73': barn spider, Araneus cavaticus
'74': garden spider, Aranea diademata
'75': black widow, Latrodectus mactans
'76': tarantula
'77': wolf spider, hunting spider
'78': tick
'79': centipede
'80': black grouse
'81': ptarmigan
'82': ruffed grouse, partridge, Bonasa umbellus
'83': prairie chicken, prairie grouse, prairie fowl
'84': peacock
'85': quail
'86': partridge
'87': African grey, African gray, Psittacus erithacus
'88': macaw
'89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
'90': lorikeet
'91': coucal
'92': bee eater
'93': hornbill
'94': hummingbird
'95': jacamar
'96': toucan
'97': drake
'98': red-breasted merganser, Mergus serrator
'99': goose
'100': black swan, Cygnus atratus
'101': tusker
'102': echidna, spiny anteater, anteater
'103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
'104': wallaby, brush kangaroo
'105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
'106': wombat
'107': jellyfish
'108': sea anemone, anemone
'109': brain coral
'110': flatworm, platyhelminth
'111': nematode, nematode worm, roundworm
'112': conch
'113': snail
'114': slug
'115': sea slug, nudibranch
'116': chiton, coat-of-mail shell, sea cradle, polyplacophore
'117': chambered nautilus, pearly nautilus, nautilus
'118': Dungeness crab, Cancer magister
'119': rock crab, Cancer irroratus
'120': fiddler crab
'121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
'122': American lobster, Northern lobster, Maine lobster, Homarus americanus
'123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
'124': crayfish, crawfish, crawdad, crawdaddy
'125': hermit crab
'126': isopod
'127': white stork, Ciconia ciconia
'128': black stork, Ciconia nigra
'129': spoonbill
'130': flamingo
'131': little blue heron, Egretta caerulea
'132': American egret, great white heron, Egretta albus
'133': bittern
'134': crane
'135': limpkin, Aramus pictus
'136': European gallinule, Porphyrio porphyrio
'137': American coot, marsh hen, mud hen, water hen, Fulica americana
'138': bustard
'139': ruddy turnstone, Arenaria interpres
'140': red-backed sandpiper, dunlin, Erolia alpina
'141': redshank, Tringa totanus
'142': dowitcher
'143': oystercatcher, oyster catcher
'144': pelican
'145': king penguin, Aptenodytes patagonica
'146': albatross, mollymawk
'147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
'148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca
'149': dugong, Dugong dugon
'150': sea lion
'151': Chihuahua
'152': Japanese spaniel
'153': Maltese dog, Maltese terrier, Maltese
'154': Pekinese, Pekingese, Peke
'155': Shih-Tzu
'156': Blenheim spaniel
'157': papillon
'158': toy terrier
'159': Rhodesian ridgeback
'160': Afghan hound, Afghan
'161': basset, basset hound
'162': beagle
'163': bloodhound, sleuthhound
'164': bluetick
'165': black-and-tan coonhound
'166': Walker hound, Walker foxhound
'167': English foxhound
'168': redbone
'169': borzoi, Russian wolfhound
'170': Irish wolfhound
'171': Italian greyhound
'172': whippet
'173': Ibizan hound, Ibizan Podenco
'174': Norwegian elkhound, elkhound
'175': otterhound, otter hound
'176': Saluki, gazelle hound
'177': Scottish deerhound, deerhound
'178': Weimaraner
'179': Staffordshire bullterrier, Staffordshire bull terrier
'180': American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
'181': Bedlington terrier
'182': Border terrier
'183': Kerry blue terrier
'184': Irish terrier
'185': Norfolk terrier
'186': Norwich terrier
'187': Yorkshire terrier
'188': wire-haired fox terrier
'189': Lakeland terrier
'190': Sealyham terrier, Sealyham
'191': Airedale, Airedale terrier
'192': cairn, cairn terrier
'193': Australian terrier
'194': Dandie Dinmont, Dandie Dinmont terrier
'195': Boston bull, Boston terrier
'196': miniature schnauzer
'197': giant schnauzer
'198': standard schnauzer
'199': Scotch terrier, Scottish terrier, Scottie
'200': Tibetan terrier, chrysanthemum dog
'201': silky terrier, Sydney silky
'202': soft-coated wheaten terrier
'203': West Highland white terrier
'204': Lhasa, Lhasa apso
'205': flat-coated retriever
'206': curly-coated retriever
'207': golden retriever
'208': Labrador retriever
'209': Chesapeake Bay retriever
'210': German short-haired pointer
'211': vizsla, Hungarian pointer
'212': English setter
'213': Irish setter, red setter
'214': Gordon setter
'215': Brittany spaniel
'216': clumber, clumber spaniel
'217': English springer, English springer spaniel
'218': Welsh springer spaniel
'219': cocker spaniel, English cocker spaniel, cocker
'220': Sussex spaniel
'221': Irish water spaniel
'222': kuvasz
'223': schipperke
'224': groenendael
'225': malinois
'226': briard
'227': kelpie
'228': komondor
'229': Old English sheepdog, bobtail
'230': Shetland sheepdog, Shetland sheep dog, Shetland
'231': collie
'232': Border collie
'233': Bouvier des Flandres, Bouviers des Flandres
'234': Rottweiler
'235': German shepherd, German shepherd dog, German police dog, alsatian
'236': Doberman, Doberman pinscher
'237': miniature pinscher
'238': Greater Swiss Mountain dog
'239': Bernese mountain dog
'240': Appenzeller
'241': EntleBucher
'242': boxer
'243': bull mastiff
'244': Tibetan mastiff
'245': French bulldog
'246': Great Dane
'247': Saint Bernard, St Bernard
'248': Eskimo dog, husky
'249': malamute, malemute, Alaskan malamute
'250': Siberian husky
'251': dalmatian, coach dog, carriage dog
'252': affenpinscher, monkey pinscher, monkey dog
'253': basenji
'254': pug, pug-dog
'255': Leonberg
'256': Newfoundland, Newfoundland dog
'257': Great Pyrenees
'258': Samoyed, Samoyede
'259': Pomeranian
'260': chow, chow chow
'261': keeshond
'262': Brabancon griffon
'263': Pembroke, Pembroke Welsh corgi
'264': Cardigan, Cardigan Welsh corgi
'265': toy poodle
'266': miniature poodle
'267': standard poodle
'268': Mexican hairless
'269': timber wolf, grey wolf, gray wolf, Canis lupus
'270': white wolf, Arctic wolf, Canis lupus tundrarum
'271': red wolf, maned wolf, Canis rufus, Canis niger
'272': coyote, prairie wolf, brush wolf, Canis latrans
'273': dingo, warrigal, warragal, Canis dingo
'274': dhole, Cuon alpinus
'275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
'276': hyena, hyaena
'277': red fox, Vulpes vulpes
'278': kit fox, Vulpes macrotis
'279': Arctic fox, white fox, Alopex lagopus
'280': grey fox, gray fox, Urocyon cinereoargenteus
'281': tabby, tabby cat
'282': tiger cat
'283': Persian cat
'284': Siamese cat, Siamese
'285': Egyptian cat
'286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
'287': lynx, catamount
'288': leopard, Panthera pardus
'289': snow leopard, ounce, Panthera uncia
'290': jaguar, panther, Panthera onca, Felis onca
'291': lion, king of beasts, Panthera leo
'292': tiger, Panthera tigris
'293': cheetah, chetah, Acinonyx jubatus
'294': brown bear, bruin, Ursus arctos
'295': American black bear, black bear, Ursus americanus, Euarctos americanus
'296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
'297': sloth bear, Melursus ursinus, Ursus ursinus
'298': mongoose
'299': meerkat, mierkat
'300': tiger beetle
'301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
'302': ground beetle, carabid beetle
'303': long-horned beetle, longicorn, longicorn beetle
'304': leaf beetle, chrysomelid
'305': dung beetle
'306': rhinoceros beetle
'307': weevil
'308': fly
'309': bee
'310': ant, emmet, pismire
'311': grasshopper, hopper
'312': cricket
'313': walking stick, walkingstick, stick insect
'314': cockroach, roach
'315': mantis, mantid
'316': cicada, cicala
'317': leafhopper
'318': lacewing, lacewing fly
'319': dragonfly, darning needle, devil's darning needle, sewing needle,
snake feeder, snake doctor, mosquito hawk, skeeter hawk
'320': damselfly
'321': admiral
'322': ringlet, ringlet butterfly
'323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
'324': cabbage butterfly
'325': sulphur butterfly, sulfur butterfly
'326': lycaenid, lycaenid butterfly
'327': starfish, sea star
'328': sea urchin
'329': sea cucumber, holothurian
'330': wood rabbit, cottontail, cottontail rabbit
'331': hare
'332': Angora, Angora rabbit
'333': hamster
'334': porcupine, hedgehog
'335': fox squirrel, eastern fox squirrel, Sciurus niger
'336': marmot
'337': beaver
'338': guinea pig, Cavia cobaya
'339': sorrel
'340': zebra
'341': hog, pig, grunter, squealer, Sus scrofa
'342': wild boar, boar, Sus scrofa
'343': warthog
'344': hippopotamus, hippo, river horse, Hippopotamus amphibius
'345': ox
'346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
'347': bison
'348': ram, tup
'349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
'350': ibex, Capra ibex
'351': hartebeest
'352': impala, Aepyceros melampus
'353': gazelle
'354': Arabian camel, dromedary, Camelus dromedarius
'355': llama
'356': weasel
'357': mink
'358': polecat, fitch, foulmart, foumart, Mustela putorius
'359': black-footed ferret, ferret, Mustela nigripes
'360': otter
'361': skunk, polecat, wood pussy
'362': badger
'363': armadillo
'364': three-toed sloth, ai, Bradypus tridactylus
'365': orangutan, orang, orangutang, Pongo pygmaeus
'366': gorilla, Gorilla gorilla
'367': chimpanzee, chimp, Pan troglodytes
'368': gibbon, Hylobates lar
'369': siamang, Hylobates syndactylus, Symphalangus syndactylus
'370': guenon, guenon monkey
'371': patas, hussar monkey, Erythrocebus patas
'372': baboon
'373': macaque
'374': langur
'375': colobus, colobus monkey
'376': proboscis monkey, Nasalis larvatus
'377': marmoset
'378': capuchin, ringtail, Cebus capucinus
'379': howler monkey, howler
'380': titi, titi monkey
'381': spider monkey, Ateles geoffroyi
'382': squirrel monkey, Saimiri sciureus
'383': Madagascar cat, ring-tailed lemur, Lemur catta
'384': indri, indris, Indri indri, Indri brevicaudatus
'385': Indian elephant, Elephas maximus
'386': African elephant, Loxodonta africana
'387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
'388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
'389': barracouta, snoek
'390': eel
'391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus
kisutch
'392': rock beauty, Holocanthus tricolor
'393': anemone fish
'394': sturgeon
'395': gar, garfish, garpike, billfish, Lepisosteus osseus
'396': lionfish
'397': puffer, pufferfish, blowfish, globefish
'398': abacus
'399': abaya
'400': academic gown, academic robe, judge's robe
'401': accordion, piano accordion, squeeze box
'402': acoustic guitar
'403': aircraft carrier, carrier, flattop, attack aircraft carrier
'404': airliner
'405': airship, dirigible
'406': altar
'407': ambulance
'408': amphibian, amphibious vehicle
'409': analog clock
'410': apiary, bee house
'411': apron
'412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
'413': assault rifle, assault gun
'414': backpack, back pack, knapsack, packsack, rucksack, haversack
'415': bakery, bakeshop, bakehouse
'416': balance beam, beam
'417': balloon
'418': ballpoint, ballpoint pen, ballpen, Biro
'419': Band Aid
'420': banjo
'421': bannister, banister, balustrade, balusters, handrail
'422': barbell
'423': barber chair
'424': barbershop
'425': barn
'426': barometer
'427': barrel, cask
'428': barrow, garden cart, lawn cart, wheelbarrow
'429': baseball
'430': basketball
'431': bassinet
'432': bassoon
'433': bathing cap, swimming cap
'434': bath towel
'435': bathtub, bathing tub, bath, tub
'436': beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
'437': beacon, lighthouse, beacon light, pharos
'438': beaker
'439': bearskin, busby, shako
'440': beer bottle
'441': beer glass
'442': bell cote, bell cot
'443': bib
'444': bicycle-built-for-two, tandem bicycle, tandem
'445': bikini, two-piece
'446': binder, ring-binder
'447': binoculars, field glasses, opera glasses
'448': birdhouse
'449': boathouse
'450': bobsled, bobsleigh, bob
'451': bolo tie, bolo, bola tie, bola
'452': bonnet, poke bonnet
'453': bookcase
'454': bookshop, bookstore, bookstall
'455': bottlecap
'456': bow
'457': bow tie, bow-tie, bowtie
'458': brass, memorial tablet, plaque
'459': brassiere, bra, bandeau
'460': breakwater, groin, groyne, mole, bulwark, seawall, jetty
'461': breastplate, aegis, egis
'462': broom
'463': bucket, pail
'464': buckle
'465': bulletproof vest
'466': bullet train, bullet
'467': butcher shop, meat market
'468': cab, hack, taxi, taxicab
'469': caldron, cauldron
'470': candle, taper, wax light
'471': cannon
'472': canoe
'473': can opener, tin opener
'474': cardigan
'475': car mirror
'476': carousel, carrousel, merry-go-round, roundabout, whirligig
'477': carpenter's kit, tool kit
'478': carton
'479': car wheel
'480': cash machine, cash dispenser, automated teller machine, automatic
teller machine, automated teller, automatic teller, ATM
'481': cassette
'482': cassette player
'483': castle
'484': catamaran
'485': CD player
'486': cello, violoncello
'487': cellular telephone, cellular phone, cellphone, cell, mobile phone
'488': chain
'489': chainlink fence
'490': chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
'491': chain saw, chainsaw
'492': chest
'493': chiffonier, commode
'494': chime, bell, gong
'495': china cabinet, china closet
'496': Christmas stocking
'497': church, church building
'498': cinema, movie theater, movie theatre, movie house, picture palace
'499': cleaver, meat cleaver, chopper
'500': cliff dwelling
'501': cloak
'502': clog, geta, patten, sabot
'503': cocktail shaker
'504': coffee mug
'505': coffeepot
'506': coil, spiral, volute, whorl, helix
'507': combination lock
'508': computer keyboard, keypad
'509': confectionery, confectionary, candy store
'510': container ship, containership, container vessel
'511': convertible
'512': corkscrew, bottle screw
'513': cornet, horn, trumpet, trump
'514': cowboy boot
'515': cowboy hat, ten-gallon hat
'516': cradle
'517': crane2
'518': crash helmet
'519': crate
'520': crib, cot
'521': Crock Pot
'522': croquet ball
'523': crutch
'524': cuirass
'525': dam, dike, dyke
'526': desk
'527': desktop computer
'528': dial telephone, dial phone
'529': diaper, nappy, napkin
'530': digital clock
'531': digital watch
'532': dining table, board
'533': dishrag, dishcloth
'534': dishwasher, dish washer, dishwashing machine
'535': disk brake, disc brake
'536': dock, dockage, docking facility
'537': dogsled, dog sled, dog sleigh
'538': dome
'539': doormat, welcome mat
'540': drilling platform, offshore rig
'541': drum, membranophone, tympan
'542': drumstick
'543': dumbbell
'544': Dutch oven
'545': electric fan, blower
'546': electric guitar
'547': electric locomotive
'548': entertainment center
'549': envelope
'550': espresso maker
'551': face powder
'552': feather boa, boa
'553': file, file cabinet, filing cabinet
'554': fireboat
'555': fire engine, fire truck
'556': fire screen, fireguard
'557': flagpole, flagstaff
'558': flute, transverse flute
'559': folding chair
'560': football helmet
'561': forklift
'562': fountain
'563': fountain pen
'564': four-poster
'565': freight car
'566': French horn, horn
'567': frying pan, frypan, skillet
'568': fur coat
'569': garbage truck, dustcart
'570': gasmask, respirator, gas helmet
'571': gas pump, gasoline pump, petrol pump, island dispenser
'572': goblet
'573': go-kart
'574': golf ball
'575': golfcart, golf cart
'576': gondola
'577': gong, tam-tam
'578': gown
'579': grand piano, grand
'580': greenhouse, nursery, glasshouse
'581': grille, radiator grille
'582': grocery store, grocery, food market, market
'583': guillotine
'584': hair slide
'585': hair spray
'586': half track
'587': hammer
'588': hamper
'589': hand blower, blow dryer, blow drier, hair dryer, hair drier
'590': hand-held computer, hand-held microcomputer
'591': handkerchief, hankie, hanky, hankey
'592': hard disc, hard disk, fixed disk
'593': harmonica, mouth organ, harp, mouth harp
'594': harp
'595': harvester, reaper
'596': hatchet
'597': holster
'598': home theater, home theatre
'599': honeycomb
'600': hook, claw
'601': hoopskirt, crinoline
'602': horizontal bar, high bar
'603': horse cart, horse-cart
'604': hourglass
'605': iPod
'606': iron, smoothing iron
'607': jack-o'-lantern
'608': jean, blue jean, denim
'609': jeep, landrover
'610': jersey, T-shirt, tee shirt
'611': jigsaw puzzle
'612': jinrikisha, ricksha, rickshaw
'613': joystick
'614': kimono
'615': knee pad
'616': knot
'617': lab coat, laboratory coat
'618': ladle
'619': lampshade, lamp shade
'620': laptop, laptop computer
'621': lawn mower, mower
'622': lens cap, lens cover
'623': letter opener, paper knife, paperknife
'624': library
'625': lifeboat
'626': lighter, light, igniter, ignitor
'627': limousine, limo
'628': liner, ocean liner
'629': lipstick, lip rouge
'630': Loafer
'631': lotion
'632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
'633': loupe, jeweler's loupe
'634': lumbermill, sawmill
'635': magnetic compass
'636': mailbag, postbag
'637': mailbox, letter box
'638': maillot
'639': maillot, tank suit
'640': manhole cover
'641': maraca
'642': marimba, xylophone
'643': mask
'644': matchstick
'645': maypole
'646': maze, labyrinth
'647': measuring cup
'648': medicine chest, medicine cabinet
'649': megalith, megalithic structure
'650': microphone, mike
'651': microwave, microwave oven
'652': military uniform
'653': milk can
'654': minibus
'655': miniskirt, mini
'656': minivan
'657': missile
'658': mitten
'659': mixing bowl
'660': mobile home, manufactured home
'661': Model T
'662': modem
'663': monastery
'664': monitor
'665': moped
'666': mortar
'667': mortarboard
'668': mosque
'669': mosquito net
'670': motor scooter, scooter
'671': mountain bike, all-terrain bike, off-roader
'672': mountain tent
'673': mouse, computer mouse
'674': mousetrap
'675': moving van
'676': muzzle
'677': nail
'678': neck brace
'679': necklace
'680': nipple
'681': notebook, notebook computer
'682': obelisk
'683': oboe, hautboy, hautbois
'684': ocarina, sweet potato
'685': odometer, hodometer, mileometer, milometer
'686': oil filter
'687': organ, pipe organ
'688': oscilloscope, scope, cathode-ray oscilloscope, CRO
'689': overskirt
'690': oxcart
'691': oxygen mask
'692': packet
'693': paddle, boat paddle
'694': paddlewheel, paddle wheel
'695': padlock
'696': paintbrush
'697': pajama, pyjama, pj's, jammies
'698': palace
'699': panpipe, pandean pipe, syrinx
'700': paper towel
'701': parachute, chute
'702': parallel bars, bars
'703': park bench
'704': parking meter
'705': passenger car, coach, carriage
'706': patio, terrace
'707': pay-phone, pay-station
'708': pedestal, plinth, footstall
'709': pencil box, pencil case
'710': pencil sharpener
'711': perfume, essence
'712': Petri dish
'713': photocopier
'714': pick, plectrum, plectron
'715': pickelhaube
'716': picket fence, paling
'717': pickup, pickup truck
'718': pier
'719': piggy bank, penny bank
'720': pill bottle
'721': pillow
'722': ping-pong ball
'723': pinwheel
'724': pirate, pirate ship
'725': pitcher, ewer
'726': plane, carpenter's plane, woodworking plane
'727': planetarium
'728': plastic bag
'729': plate rack
'730': plow, plough
'731': plunger, plumber's helper
'732': Polaroid camera, Polaroid Land camera
'733': pole
'734': police van, police wagon, paddy wagon, patrol wagon, wagon, black
Maria
'735': poncho
'736': pool table, billiard table, snooker table
'737': pop bottle, soda bottle
'738': pot, flowerpot
'739': potter's wheel
'740': power drill
'741': prayer rug, prayer mat
'742': printer
'743': prison, prison house
'744': projectile, missile
'745': projector
'746': puck, hockey puck
'747': punching bag, punch bag, punching ball, punchball
'748': purse
'749': quill, quill pen
'750': quilt, comforter, comfort, puff
'751': racer, race car, racing car
'752': racket, racquet
'753': radiator
'754': radio, wireless
'755': radio telescope, radio reflector
'756': rain barrel
'757': recreational vehicle, RV, R.V.
'758': reel
'759': reflex camera
'760': refrigerator, icebox
'761': remote control, remote
'762': restaurant, eating house, eating place, eatery
'763': revolver, six-gun, six-shooter
'764': rifle
'765': rocking chair, rocker
'766': rotisserie
'767': rubber eraser, rubber, pencil eraser
'768': rugby ball
'769': rule, ruler
'770': running shoe
'771': safe
'772': safety pin
'773': saltshaker, salt shaker
'774': sandal
'775': sarong
'776': sax, saxophone
'777': scabbard
'778': scale, weighing machine
'779': school bus
'780': schooner
'781': scoreboard
'782': screen, CRT screen
'783': screw
'784': screwdriver
'785': seat belt, seatbelt
'786': sewing machine
'787': shield, buckler
'788': shoe shop, shoe-shop, shoe store
'789': shoji
'790': shopping basket
'791': shopping cart
'792': shovel
'793': shower cap
'794': shower curtain
'795': ski
'796': ski mask
'797': sleeping bag
'798': slide rule, slipstick
'799': sliding door
'800': slot, one-armed bandit
'801': snorkel
'802': snowmobile
'803': snowplow, snowplough
'804': soap dispenser
'805': soccer ball
'806': sock
'807': solar dish, solar collector, solar furnace
'808': sombrero
'809': soup bowl
'810': space bar
'811': space heater
'812': space shuttle
'813': spatula
'814': speedboat
'815': spider web, spider's web
'816': spindle
'817': sports car, sport car
'818': spotlight, spot
'819': stage
'820': steam locomotive
'821': steel arch bridge
'822': steel drum
'823': stethoscope
'824': stole
'825': stone wall
'826': stopwatch, stop watch
'827': stove
'828': strainer
'829': streetcar, tram, tramcar, trolley, trolley car
'830': stretcher
'831': studio couch, day bed
'832': stupa, tope
'833': submarine, pigboat, sub, U-boat
'834': suit, suit of clothes
'835': sundial
'836': sunglass
'837': sunglasses, dark glasses, shades
'838': sunscreen, sunblock, sun blocker
'839': suspension bridge
'840': swab, swob, mop
'841': sweatshirt
'842': swimming trunks, bathing trunks
'843': swing
'844': switch, electric switch, electrical switch
'845': syringe
'846': table lamp
'847': tank, army tank, armored combat vehicle, armoured combat vehicle
'848': tape player
'849': teapot
'850': teddy, teddy bear
'851': television, television system
'852': tennis ball
'853': thatch, thatched roof
'854': theater curtain, theatre curtain
'855': thimble
'856': thresher, thrasher, threshing machine
'857': throne
'858': tile roof
'859': toaster
'860': tobacco shop, tobacconist shop, tobacconist
'861': toilet seat
'862': torch
'863': totem pole
'864': tow truck, tow car, wrecker
'865': toyshop
'866': tractor
'867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
semi
'868': tray
'869': trench coat
'870': tricycle, trike, velocipede
'871': trimaran
'872': tripod
'873': triumphal arch
'874': trolleybus, trolley coach, trackless trolley
'875': trombone
'876': tub, vat
'877': turnstile
'878': typewriter keyboard
'879': umbrella
'880': unicycle, monocycle
'881': upright, upright piano
'882': vacuum, vacuum cleaner
'883': vase
'884': vault
'885': velvet
'886': vending machine
'887': vestment
'888': viaduct
'889': violin, fiddle
'890': volleyball
'891': waffle iron
'892': wall clock
'893': wallet, billfold, notecase, pocketbook
'894': wardrobe, closet, press
'895': warplane, military plane
'896': washbasin, handbasin, washbowl, lavabo, wash-hand basin
'897': washer, automatic washer, washing machine
'898': water bottle
'899': water jug
'900': water tower
'901': whiskey jug
'902': whistle
'903': wig
'904': window screen
'905': window shade
'906': Windsor tie
'907': wine bottle
'908': wing
'909': wok
'910': wooden spoon
'911': wool, woolen, woollen
'912': worm fence, snake fence, snake-rail fence, Virginia fence
'913': wreck
'914': yawl
'915': yurt
'916': web site, website, internet site, site
'917': comic book
'918': crossword puzzle, crossword
'919': street sign
'920': traffic light, traffic signal, stoplight
'921': book jacket, dust cover, dust jacket, dust wrapper
'922': menu
'923': plate
'924': guacamole
'925': consomme
'926': hot pot, hotpot
'927': trifle
'928': ice cream, icecream
'929': ice lolly, lolly, lollipop, popsicle
'930': French loaf
'931': bagel, beigel
'932': pretzel
'933': cheeseburger
'934': hotdog, hot dog, red hot
'935': mashed potato
'936': head cabbage
'937': broccoli
'938': cauliflower
'939': zucchini, courgette
'940': spaghetti squash
'941': acorn squash
'942': butternut squash
'943': cucumber, cuke
'944': artichoke, globe artichoke
'945': bell pepper
'946': cardoon
'947': mushroom
'948': Granny Smith
'949': strawberry
'950': orange
'951': lemon
'952': fig
'953': pineapple, ananas
'954': banana
'955': jackfruit, jak, jack
'956': custard apple
'957': pomegranate
'958': hay
'959': carbonara
'960': chocolate sauce, chocolate syrup
'961': dough
'962': meat loaf, meatloaf
'963': pizza, pizza pie
'964': potpie
'965': burrito
'966': red wine
'967': espresso
'968': cup
'969': eggnog
'970': alp
'971': bubble
'972': cliff, drop, drop-off
'973': coral reef
'974': geyser
'975': lakeside, lakeshore
'976': promontory, headland, head, foreland
'977': sandbar, sand bar
'978': seashore, coast, seacoast, sea-coast
'979': valley, vale
'980': volcano
'981': ballplayer, baseball player
'982': groom, bridegroom
'983': scuba diver
'984': rapeseed
'985': daisy
'986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
Cypripedium parviflorum
'987': corn
'988': acorn
'989': hip, rose hip, rosehip
'990': buckeye, horse chestnut, conker
'991': coral fungus
'992': agaric
'993': gyromitra
'994': stinkhorn, carrion fungus
'995': earthstar
'996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola
frondosa
'997': bolete
'998': ear, spike, capitulum
'999': toilet tissue, toilet paper, bathroom tissue
- name: cls_token
sequence:
sequence: float32
- name: patch_tokens
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 20224716800
num_examples: 12800
- name: validation
num_bytes: 7900280000
num_examples: 5000
download_size: 23726734894
dataset_size: 28124996800
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "imagenet_dino"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Jack0508/eng_vi_demo | Jack0508 | 2021-11-07T14:26:53Z | 153 | 0 | null | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
LysandreJik/temp-repo-valid | LysandreJik | 2021-08-10T15:01:23Z | 153 | 0 | null | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
JustinE/Test | JustinE | 2022-01-18T18:18:29Z | 153 | 0 | null | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Niciu/test-squad | Niciu | 2022-02-27T12:17:15Z | 153 | 0 | null | [
"region:us"
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NishinoTSK/leishmaniaV2 | NishinoTSK | 2022-02-17T19:37:59Z | 153 | 0 | null | [
"region:us"
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Shanna/Jamaica | Shanna | 2021-12-10T04:21:53Z | 153 | 0 | null | [
"region:us"
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ShinyQ/PPKM_Pemerintah | ShinyQ | 2021-10-02T03:47:28Z | 153 | 0 | null | [
"region:us"
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TRoboto/masc | TRoboto | 2021-08-16T19:34:57Z | 153 | 0 | null | [
"region:us"
] | 2021-08-16T19:34:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | #MASC
The dataset will be available soon. | [
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TaahaKazi/FCE | TaahaKazi | 2021-12-02T18:21:34Z | 153 | 0 | null | [
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Terry0107/RiSAWOZ | Terry0107 | 2021-03-21T03:16:45Z | 153 | 0 | null | [
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TestCher/Testi | TestCher | 2022-02-10T09:07:54Z | 153 | 0 | null | [
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TheBlindBandit/SpongeNot | TheBlindBandit | 2021-09-03T19:22:33Z | 153 | 0 | null | [
"region:us"
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TimTreasure4/Test | TimTreasure4 | 2021-03-17T07:12:59Z | 153 | 0 | null | [
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Trainmaster9977/957 | Trainmaster9977 | 2021-05-01T02:34:50Z | 153 | 0 | null | [
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Trainmaster9977/zbakuman | Trainmaster9977 | 2021-05-01T17:42:35Z | 153 | 0 | null | [
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Tyler/wikimatrix_collapsed | Tyler | 2021-04-13T19:54:24Z | 153 | 0 | null | [
"region:us"
] | 2021-04-13T19:54:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Transformation of AI.FB's Wikimatrix dataset. Combined rows containing translations of a single source sentence into one consolidated row, applying a score threshold of 1.03 to remove poor translations. | [
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Usin2705/test | Usin2705 | 2021-12-07T21:02:58Z | 153 | 0 | null | [
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Vishnu393831/VICTORY_dataset | Vishnu393831 | 2022-02-12T04:21:20Z | 153 | 0 | null | [
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] | 2022-02-12T04:21:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: afl-3.0
---
| [
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Vishva/UniFAQ_DataSET | Vishva | 2021-03-07T06:14:23Z | 153 | 0 | null | [
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Wiedy/be | Wiedy | 2021-12-07T09:44:47Z | 153 | 0 | null | [
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Wiedy/wav2vec2-large-xls-r-300m-tr-colab | Wiedy | 2021-12-07T10:22:48Z | 153 | 0 | null | [
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Wikidepia/mc4-filter | Wikidepia | 2021-08-03T11:19:52Z | 153 | 0 | null | [
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Wuhu0/output | Wuhu0 | 2021-10-29T08:00:02Z | 153 | 0 | null | [
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XiangPan/iflytek | XiangPan | 2021-10-02T04:27:12Z | 153 | 0 | null | [
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XiangXiang/clt | XiangXiang | 2021-04-28T02:08:29Z | 153 | 0 | null | [
"region:us"
] | 2021-04-28T02:08:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | My new dataset | [
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Yatoro/github_issues | Yatoro | 2021-11-19T01:22:57Z | 153 | 0 | null | [
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abidlabs/crowdsourced-notes | abidlabs | 2022-01-21T15:58:14Z | 153 | 0 | null | [
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abidlabs/crowdsourced-speech6 | abidlabs | 2022-01-21T17:00:29Z | 153 | 0 | null | [
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abidlabs/voice-verification-adversarial-dataset | abidlabs | 2022-01-07T01:03:24Z | 153 | 0 | null | [
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abwicke/C-B-R | abwicke | 2021-03-19T16:45:29Z | 153 | 0 | null | [
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license: afl-3.0
---
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ajmbell/test-dataset | ajmbell | 2021-03-22T15:55:56Z | 153 | 0 | null | [
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akhaliq/test | akhaliq | 2022-02-18T03:09:27Z | 153 | 0 | null | [
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alexantonov/chuvash_parallel | alexantonov | 2022-10-24T15:26:28Z | 153 | 4 | null | [
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"language:cv",
"region:us"
] | 2022-10-24T15:26:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- cv
multilinguality:
- translation
source_datasets:
- original
task_ids:
- machine-translation
---
# Dataset Description
## Chuvash-Russian parallel corpus
1M parallel sentences. Manually aligned
## Chuvash-English parallel corpus.
200K parallel sentences. Automatically aligned
## Contributions
For additional details contact [@AlAntonov](https://github.com/AlAntonov). | [
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astarostap/antisemitic_tweets | astarostap | 2021-02-06T14:46:47Z | 153 | 0 | null | [
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atelders/politweets | atelders | 2021-08-19T12:28:45Z | 153 | 0 | null | [
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bingzhen/test2 | bingzhen | 2022-01-17T08:01:12Z | 153 | 0 | null | [
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bryantpwhite/Medieval_Sermons_in_French | bryantpwhite | 2022-01-05T17:34:14Z | 153 | 0 | null | [
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bs-modeling-metadata/wiki_dump | bs-modeling-metadata | 2021-11-23T18:54:48Z | 153 | 0 | null | [
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