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
7a15c45625e730c0ec4a33cb5eba9ef24671290d
BasSabretooth/amongstotherthings
[ "license:other", "region:us" ]
2023-05-15T12:39:33+00:00
{"license": "other"}
2023-05-15T12:40:39+00:00
c8526ed62f1ba3a4f32cc83eb732b612491b4ec8
# Dataset Card for "deec2759" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/deec2759
[ "region:us" ]
2023-05-15T12:45:21+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1329, "dataset_size": 182}}
2023-05-15T12:45:22+00:00
b965e9712cec95d657a245232a8219f7c9578aff
# Dataset Card for "ebc52d2b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/ebc52d2b
[ "region:us" ]
2023-05-15T12:58:35+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1342, "dataset_size": 186}}
2023-05-15T12:58:36+00:00
69c8fb38d7be62f74ea77364b8011ae7de6643db
# Dataset Card for "747e7d53" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/747e7d53
[ "region:us" ]
2023-05-15T12:59:58+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1341, "dataset_size": 180}}
2023-05-15T12:59:59+00:00
8902441c2261943e6d7f207ac22283a663749649
g4m3r/LS23
[ "license:mit", "region:us" ]
2023-05-15T13:02:01+00:00
{"license": "mit"}
2023-05-15T13:07:42+00:00
fd7f0aedca3ac2ee85aa323f45caf52b0d5b15c1
# Dataset Card for "RestMex2023_unsupervized-corpus_DataAugV3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vg055/RestMex2023_unsupervized-corpus_DataAugV3
[ "region:us" ]
2023-05-15T13:20:28+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 135500511, "num_examples": 363560}], "download_size": 81087280, "dataset_size": 135500511}}
2023-05-15T13:51:08+00:00
0a3063bebfc4c28c6e0d3cb5a9a3f972409fd649
# Dataset Card for "analisis-sentimientos-textos-turisitcos-mx-polaridadV3-DA" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vg055/analisis-sentimientos-textos-turisitcos-mx-polaridadV3-DA
[ "region:us" ]
2023-05-15T13:29:11+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 113820848, "num_examples": 274649}, {"name": "test", "num_bytes": 10317131, "num_examples": 25171}], "download_size": 67940473, "dataset_size": 124137979}}
2023-05-15T13:45:56+00:00
e864397700da3e418b9ddbb0ede031b96e4771a7
# Dataset Card for "f2585c11" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/f2585c11
[ "region:us" ]
2023-05-15T13:29:56+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 188, "num_examples": 10}], "download_size": 1320, "dataset_size": 188}}
2023-05-15T13:29:57+00:00
d9bef98404f316b18cffaa05e8552c27e1c9d9af
# Dataset Card for "d025b660" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d025b660
[ "region:us" ]
2023-05-15T13:29:59+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 188, "num_examples": 10}], "download_size": 1320, "dataset_size": 188}}
2023-05-15T13:30:00+00:00
626c931566a7ccd8f9d95b8e0b1f83fca328a9d2
# Dataset Card for "test-github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hyn/test-github-issues
[ "region:us" ]
2023-05-15T13:33:10+00:00
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "int64"}, {"name": "updated_at", "dtype": "int64"}, {"name": "due_on", "dtype": "int64"}, {"name": "closed_at", "dtype": "int64"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "int64"}, {"name": "updated_at", "dtype": "int64"}, {"name": "closed_at", "dtype": "int64"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}]}, {"name": "body", "dtype": "string"}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 10233851, "num_examples": 3019}], "download_size": 3012997, "dataset_size": 10233851}}
2023-05-15T13:33:13+00:00
adf34821abb1b7637239255f382be77e041f8d62
helezabi/fcb_scan
[ "license:unknown", "region:us" ]
2023-05-15T13:33:46+00:00
{"license": "unknown"}
2023-05-15T13:33:46+00:00
9c7551bba82db520bad5fa12e011acc987cc5852
A collection of images of Lighting Images to be used in ControlNet for the <a href="https://prompthero.com/academy/courses">PromptHero Academy Students</a>.
prompthero-diffusion-models/Lighting-For-ControlNet
[ "region:us" ]
2023-05-15T13:52:32+00:00
{}
2023-05-15T13:56:49+00:00
d092bd188c5b25fdc36f42ab61e343a7ead0d6b3
# Dataset Card for "Multi_restaurants_menus_translation" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
PaulineSanchez/Multi_restaurants_menus_translation
[ "region:us" ]
2023-05-15T13:56:08+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 56195, "num_examples": 503}], "download_size": 36115, "dataset_size": 56195}}
2023-05-15T13:56:12+00:00
626c8ad061ade60f5c481ee9ee96d2b6ebf0cea4
OdiaGenAI/hardcode_odia_qa_105
[ "license:cc-by-nc-4.0", "region:us" ]
2023-05-15T14:04:35+00:00
{"license": "cc-by-nc-4.0"}
2023-05-15T14:05:05+00:00
5a66577a67ae664260a38d0c712c6c0bccee06f9
290,544 posts of roleplay forum data scraped by a third party. The source data is not available here. It should be effective when used to finetune for one-one roleplay and creative writing. Additionally, it may help to generate various fanfiction-style writing and scenarios. The `dataset.yaml` file contains the SHA512 hash of the source data and accurately describes each step resulting in this dataset. This dataset has been cleaned and formatted for use with fastchat. ![Plot](assets/full-train.png) ![Plot](assets/pruned-train.png)
Squish42/bluemoon-fandom-1-1-rp-cleaned
[ "task_categories:conversational", "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:wtfpl", "not-for-all-audiences", "roleplay", "creative", "region:us" ]
2023-05-15T14:06:59+00:00
{"language": ["en"], "license": "wtfpl", "size_categories": ["100K<n<1M"], "task_categories": ["conversational", "text-generation"], "pretty_name": "Bluemoon - Fandom 1x1 Roleplay", "tags": ["not-for-all-audiences", "roleplay", "creative"]}
2023-07-09T21:35:05+00:00
17e7574d7327dd0a889685f700227a459964fd45
# Dataset Card for "dataset_food_translation_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
PaulineSanchez/dataset_food_translation_v2
[ "region:us" ]
2023-05-15T14:22:24+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 319675, "num_examples": 3656}], "download_size": 0, "dataset_size": 319675}}
2023-05-16T13:45:28+00:00
9530c880609509148350a22c6bb1397e6a9e8317
# Dataset Card for "testmodelcardwdata" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/testmodelcardwdata
[ "region:us" ]
2023-05-15T14:26:01+00:00
{"dataset_info": {"features": [{"name": "modelId", "dtype": "string"}, {"name": "sha", "dtype": "null"}, {"name": "lastModified", "dtype": "null"}, {"name": "pipeline_tag", "dtype": "string"}, {"name": "author", "dtype": "null"}, {"name": "securityStatus", "dtype": "null"}, {"name": "likes", "dtype": "int64"}, {"name": "downloads", "dtype": "int64"}, {"name": "dataset", "sequence": "string"}, {"name": "arxiv", "sequence": "string"}, {"name": "license", "sequence": "string"}, {"name": "tags", "sequence": "string"}, {"name": "doi", "sequence": "string"}, {"name": "card", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 541057, "num_examples": 100}], "download_size": 163196, "dataset_size": 541057}}
2023-05-15T14:26:04+00:00
eca1de4f8906d878cb813593a0852bcd2410d63a
Shaharb/us-social-security-medicare-FAQs-test
[ "license:mit", "region:us" ]
2023-05-15T14:31:16+00:00
{"license": "mit"}
2023-05-15T14:33:25+00:00
7b85b2555b8327eaf9d4fcde5c95af53ca717c63
# Dataset Card for "Dataset_food_translation_fr_en" - info: This dataset is the combination of two datasets I previously made . - There is : https://huggingface.co/datasets/PaulineSanchez/Trad_food which is made from the ANSES-CIQUAL 2020 Table in English in XML format, found on https://www.data.gouv.fr/fr/datasets/table-de-composition-nutritionnelle-des-aliments-ciqual/ . I made some minor changes on it in order to have it meets my needs (removed/added words to have exact translations, removed repetitions etc). - And : https://huggingface.co/datasets/PaulineSanchez/Multi_restaurants_menus_translation which is made of the translations of different menus in different restaurants. I used the menus of these different restaurants : https://salutbaramericain.com/edina/menus/ , https://menuonline.fr/legeorgev, https://www.covedina.com/menu/, https://menuonline.fr/fouquets/cartes, https://www.theavocadoshow.com/fr/food, https://papacionuparis.fr/carte/. I also made some minor changes on these menus in order to have a dataset that meets my needs. I have absolutely no connection with these restaurants and their menus are certainly subject to change.
PaulineSanchez/Dataset_food_translation_fr_en
[ "task_categories:translation", "size_categories:1K<n<10K", "language:fr", "language:en", "food", "restaurant", "menus", "nutrition", "region:us" ]
2023-05-15T14:39:03+00:00
{"language": ["fr", "en"], "size_categories": ["1K<n<10K"], "task_categories": ["translation"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "fr"]}}}], "splits": [{"name": "train", "num_bytes": 255634.8588621444, "num_examples": 2924}, {"name": "validation", "num_bytes": 63996.14113785558, "num_examples": 732}], "download_size": 208288, "dataset_size": 319631.0}, "tags": ["food", "restaurant", "menus", "nutrition"]}
2023-05-16T13:46:06+00:00
35e7c89caf6dbf3fe9333191b5e0516537681c0d
# Dataset Card for "0ca7bb7b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/0ca7bb7b
[ "region:us" ]
2023-05-15T14:42:01+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1337, "dataset_size": 182}}
2023-05-15T14:42:02+00:00
49306dd2cc1f9a0e4087976fa2e81d0562db9064
Kasuzu/Laboral_db
[ "license:other", "region:us" ]
2023-05-15T14:45:27+00:00
{"license": "other"}
2023-05-15T14:45:52+00:00
361835a57314a8fdf5acccc8c9170feb20b77de3
# Dataset Card for "audiofeaturesalbumcovers" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ramgus/audiofeaturesalbumcovers
[ "region:us" ]
2023-05-15T14:48:51+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 636771303.2, "num_examples": 1200}], "download_size": 520392718, "dataset_size": 636771303.2}}
2023-05-15T14:49:42+00:00
a93c376c2777762f41c18ede0e264e5f40a57a8a
# Dataset Card for "portuguese_xlsum" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
arubenruben/portuguese_xlsum
[ "region:us" ]
2023-05-15T15:01:34+00:00
{"dataset_info": {"features": [{"name": "summary", "dtype": "string"}, {"name": "document", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 239702693, "num_examples": 57402}, {"name": "test", "num_bytes": 22908233, "num_examples": 7175}, {"name": "validation", "num_bytes": 23068988, "num_examples": 7175}], "download_size": 173770782, "dataset_size": 285679914}}
2023-05-15T15:02:22+00:00
5ef7e580f890aa6b7afbf16d47865bebf7006e2f
# h2oGPT DataBase Data Card ## Summary H2O.ai's Chroma database files for h2oGPT for LangChain integration. Sources are generated and processed by [get_db()](https://github.com/h2oai/h2ogpt/blob/40780bc6f4197e7f54753d40adafabe7c6e582f0/gpt_langchain.py#L491-L492) | File |Purpose | Source | License |-------------|----------------|-------------------|---------- |[db_dir_DriverlessAI_docs.zip](db_dir_DriverlessAI_docs.zip) | DriverlessAI Documentation Q/A | [Source](https://github.com/h2oai/h2ogpt/blob/40780bc6f4197e7f54753d40adafabe7c6e582f0/gpt_langchain.py#L469-L473) | CC-BY-NC |[db_dir_UserData.zip](db_dir_UserData.zip) | Example PDFs and Text Files Q/A | [Source](https://github.com/h2oai/h2ogpt/blob/40780bc6f4197e7f54753d40adafabe7c6e582f0/gpt_langchain.py#L474-L478) | ArXiv |[db_dir_github_h2oGPT.zip](db_dir_github_h2oGPT.zip) | h2oGPT GitHub repo Q/A | [Source](https://github.com/h2oai/h2ogpt/blob/40780bc6f4197e7f54753d40adafabe7c6e582f0/gpt_langchain.py#L463-L468) | Apache V2 |[db_dir_wiki.zip](db_dir_wiki.zip) | Example subset of Wikipedia (from API) Q/A | [Source](https://github.com/h2oai/h2ogpt/blob/40780bc6f4197e7f54753d40adafabe7c6e582f0/gpt_langchain.py#L463-L468) | Wikipedia CC-BY-SA |[db_dir_wiki_full.zip](db_dir_wiki.zip) | All Wikipedia as of 04/01/2023 for articles with >5k views for Q/A | [Source](https://github.com/h2oai/h2ogpt/blob/40780bc6f4197e7f54753d40adafabe7c6e582f0/gpt_langchain.py#L448-L457) | Wikipedia CC-BY-SA UserData can be generated for any collection of private offline docs by running [make_db.py](https://github.com/h2oai/h2ogpt/blob/8bde589f1c532c6fb6badb313b073761ddc31f73/make_db.py#L15-L22). For quickly using a private document collection for Q/A, place documents (PDFs, text, etc.) into a folder called `user_path` and run ```bash python make_db.py ``` To use the chatbot with such docs, run: ```bash python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b --langchain_mode=UserData ``` using [h2oGPT](https://github.com/h2oai/h2ogpt) . Any other instruct-tuned base model can be used, including non-h2oGPT ones, as long as required GPU memory is avaialble for given model size. Or one can choose 8-bit generation. See also LangChain example use with [test_langchain_simple.py](https://github.com/h2oai/h2ogpt/blob/4637531b928dfa458d708615ebd2cb6454d23064/tests/test_langchain_simple.py) If one has obtained all databases (except wiki_full) and unzipped them into the current directory, then one can run h2oGPT Chatbot like: ```bash python generate.py --base_model=h2oai/h2ogpt-oasst1-512-12b --load_8bit=True --langchain_mode=UserData --visible_langchain_modes="['UserData', 'wiki', 'MyData', 'github h2oGPT', 'DriverlessAI docs']" ``` which uses now 12B model in 8-bit mode, that fits onto single 24GB GPU. If one has obtained all databases (including wiki_full) and unzipped them into the current directory, then one can run h2oGPT Chatbot like: ```bash python generate.py --base_model=h2oai/h2ogpt-oasst1-512-12b --load_8bit=True --langchain_mode=wiki_full --visible_langchain_modes="['UserData', 'wiki_full', 'MyData', 'github h2oGPT', 'DriverlessAI docs']" ``` which will default to wiki_full for QA against full Wikipedia.
h2oai/db_dirs
[ "language:en", "license:other", "gpt", "llm", "large language model", "region:us" ]
2023-05-15T15:02:18+00:00
{"language": ["en"], "license": "other", "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico", "tags": ["gpt", "llm", "large language model"]}
2023-05-27T04:39:49+00:00
a4d703274c5345b4eb9c1f9e1f0c7266d2a197eb
medmac01/qa_morocco_history_v1
[ "task_categories:question-answering", "size_categories:1K<n<10K", "language:fr", "language:en", "extractive_qa", "region:us" ]
2023-05-15T15:08:00+00:00
{"language": ["fr", "en"], "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "tags": ["extractive_qa"]}
2023-05-15T15:21:07+00:00
288d10ca7ca36a6687747b2ad3ee0ee05da75f13
# Dataset Card for "test_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
markbotterill/test_dataset
[ "region:us" ]
2023-05-15T15:18:35+00:00
{"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2650754, "num_examples": 29825}], "download_size": 1492606, "dataset_size": 2650754}}
2023-05-15T15:18:42+00:00
dc61a3820b05945bde775278476be271510c2df1
Cacau/daedraport
[ "license:creativeml-openrail-m", "region:us" ]
2023-05-15T15:26:30+00:00
{"license": "creativeml-openrail-m"}
2023-05-15T18:20:15+00:00
5e036fc9fa8c0475ad4a310d78c8f8013a8ac14a
# Dataset Card for "TVCG_NER" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Yamei/TVCG_NER
[ "region:us" ]
2023-05-15T15:44:23+00:00
{"dataset_info": {"features": [{"name": "entities", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 23659235, "num_examples": 33012}], "download_size": 8412973, "dataset_size": 23659235}}
2023-05-15T15:44:25+00:00
12fc5b65b60eb79493d7ebc65dd6e70fed6fd53f
This is a vocabulary of (semiotic_class, normalized, non-normalized, freq) tuples from [Google Text Normalization Dataset](https://www.kaggle.com/datasets/richardwilliamsproat/text-normalization-for-english-russian-and-polish). It was generated using [this script](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_normalization_as_tagging/evaluation/get_multi_reference_vocab.py). It can be used to perform fast text normalization, see example in this [script](https://github.com/bene-ges/nemo_compatible/blob/main/scripts/nlp/en_spellmapper/dataset_preparation/normalize_by_gtn_vocab.py)
bene-ges/en_gtn_vocab
[ "size_categories:1M<n<10M", "language:en", "license:cc-by-sa-4.0", "text normalization", "inverse text normalization", "region:us" ]
2023-05-15T15:46:32+00:00
{"language": ["en"], "license": "cc-by-sa-4.0", "size_categories": ["1M<n<10M"], "tags": ["text normalization", "inverse text normalization"]}
2023-08-04T08:23:38+00:00
a6a5038d19fdab48c4f9c688b3d1443a62527805
HK83/Anime
[ "license:afl-3.0", "region:us" ]
2023-05-15T15:53:09+00:00
{"license": "afl-3.0"}
2023-05-15T19:50:56+00:00
693d9ca44545ab559f58f9c6d0a19d3f57026314
# Dataset Card for "TestDataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ernestum/TestDataset
[ "region:us" ]
2023-05-15T15:58:12+00:00
{"dataset_info": {"features": [{"name": "obs", "sequence": {"sequence": "float32"}}, {"name": "acts", "sequence": "int64"}, {"name": "infos", "sequence": "string"}, {"name": "terminal", "dtype": "bool"}, {"name": "rews", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 72338, "num_examples": 12}], "download_size": 18743, "dataset_size": 72338}}
2023-05-15T16:03:26+00:00
859ff75f8b377859d3d8b58244564789bb81c6de
# Dataset Card for "timeseries-daily-sp500" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://edarchimbaud.substack.com - **Repository:** https://github.com/edarchimbaud - **Point of Contact:** [email protected] ### Dataset Summary The timeseries-daily-sp500 dataset provides daily historical data for companies in the S&P 500 index. ### Supported Tasks and Leaderboards The dataset can be used to train a model for systematic trading. The model performance is evaluated based on the return / risk profile of the positions taken by the model. ### Languages [N/A] ## Dataset Structure ### Data Instances [N/A] ### Data Fields - symbol (string): A string representing the ticker symbol or abbreviation used to identify the company. - date (timestamp[ns, tz=America/New_York]): A timestamp indicating the date of the recorded data. The timestamps are in the America/New_York time zone. - open (float64): A floating-point number representing the opening price of the stock on the given date. - high (float64): A floating-point number representing the highest price of the stock on the given date. - low (float64): A floating-point number representing the lowest price of the stock on the given date. - close (float64): A floating-point number representing the closing price of the stock on the given date. - volume (int64): An integer indicating the trading volume (number of shares) of the stock on the given date. - dividends (float64): A floating-point number representing the dividends paid by the stock on the given date. - stock_splits (float64): A floating-point number representing any stock splits that occurred on the given date. ### Data Splits A single split, called train. ## Dataset Creation ### Curation Rationale The timeseries-daily-sp500 dataset was developed to support the development of low-frequency trading algorithms. ### Source Data #### Initial Data Collection and Normalization This data was sourced from the web, and aggregated. ### Annotations #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information [N/A] ## Considerations for Using the Data ### Social Impact of Dataset [N/A] ### Discussion of Biases [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators The timeseries-daily-sp500 dataset was collected by https://edarchimbaud.substack.com. ### Licensing Information The timeseries-daily-sp500 dataset is licensed under the MIT License. ### Citation Information > https://edarchimbaud.substack.com, timeseries-daily-sp500 dataset, GitHub repository, https://github.com/edarchimbaud ### Contributions Thanks to [@edarchimbaud](https://github.com/edarchimbaud) for adding this dataset.
edarchimbaud/timeseries-1d-stocks
[ "task_categories:tabular-regression", "language:en", "license:mit", "region:us" ]
2023-05-15T16:02:00+00:00
{"language": ["en"], "license": "mit", "task_categories": ["tabular-regression"], "dataset_info": {"features": [{"name": "symbol", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "open", "dtype": "float64"}, {"name": "high", "dtype": "float64"}, {"name": "low", "dtype": "float64"}, {"name": "close", "dtype": "float64"}, {"name": "adj_close", "dtype": "float64"}, {"name": "volume", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 598131989, "num_examples": 8535427}], "download_size": 296223107, "dataset_size": 598131989}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2023-11-21T05:07:10+00:00
8bcbff43b19dec214f5ce40495fdca57765bdd45
smithr14buckeye/whistlingflagsdataset
[ "language:en", "region:us" ]
2023-05-15T16:19:19+00:00
{"language": ["en"], "pretty_name": "Hole and Round Stats"}
2023-05-15T16:50:23+00:00
1f5a2b6cb699b2f9087b114879970be6f77ed8e0
# Dataset Card for "b47a6b0d" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b47a6b0d
[ "region:us" ]
2023-05-15T16:21:25+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1340, "dataset_size": 178}}
2023-05-15T16:21:26+00:00
62fd65f4763c312adb6e38d6d7c98f689598cbb7
# Dataset Card for "a87bd4e2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/a87bd4e2
[ "region:us" ]
2023-05-15T16:21:27+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1340, "dataset_size": 178}}
2023-05-15T16:21:28+00:00
288b066c37879d86b047390795a4b240f1d68b1b
wangweizhi98/FLAIR
[ "license:apache-2.0", "region:us" ]
2023-05-15T16:36:23+00:00
{"license": "apache-2.0"}
2023-05-15T16:36:23+00:00
18e112d3b070fa58f92a3a21e3dd91809d21bd03
rajaneeshr/deta
[ "license:gpl-3.0", "region:us" ]
2023-05-15T16:44:20+00:00
{"license": "gpl-3.0"}
2023-05-15T16:44:20+00:00
0858fae5ea628fd208422ac64d20f351c0be423e
# Dataset Card for "FLAN_CoT_alpaca_style" We provide a dataset representing the 9 chain-of-thought (reasoning) fine-tuning tasks from [FLAN](https://arxiv.org/pdf/2210.11416.pdf). Minor formatting has been applied: - We apply an Alpaca-style format (i.e. instruction/input/output fields) - If the question is multiple-choice, the options are provided in the input field - The phrase "Explain your reasoning step-by-step before providing the correct answer." is added to the end of the instruction field. Numbers: Prompts: 74771 Tokens: 9016176 using the EleutherAI/gpt-neox-20b tokenizer (counting instruction+input+output)
lucasmccabe-lmi/FLAN_CoT_alpaca_style
[ "arxiv:2210.11416", "region:us" ]
2023-05-15T17:22:04+00:00
{"dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37140971, "num_examples": 74771}], "download_size": 14062550, "dataset_size": 37140971}}
2023-05-15T17:28:43+00:00
ebd49cb29e60cb8ef0433bbbc1d09d8c8fe07ab6
# Dataset Card for "lmd_clean_8bars_32th_resolution" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
juancopi81/lmd_clean_8bars_32th_resolution
[ "region:us" ]
2023-05-15T18:07:52+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6417909784, "num_examples": 244436}, {"name": "test", "num_bytes": 1221971111, "num_examples": 46005}, {"name": "validation", "num_bytes": 1465985310, "num_examples": 54947}], "download_size": 974110589, "dataset_size": 9105866205}}
2023-05-15T18:10:29+00:00
b1b860e57c2dfe803b41935c232294ba8885a497
# Dataset Card for "covid-qa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pranjali97/covid-qa
[ "region:us" ]
2023-05-15T18:28:14+00:00
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "document_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48664845, "num_examples": 1417}, {"name": "validation", "num_bytes": 4316222, "num_examples": 203}, {"name": "test", "num_bytes": 11611421, "num_examples": 375}], "download_size": 0, "dataset_size": 64592488}}
2023-05-15T22:37:25+00:00
f93ce9c6fb60d52985cbc4891c328fe170256357
sam-liu-lmi/databricks-dolly-15k-alpaca-style
[ "license:apache-2.0", "region:us" ]
2023-05-15T18:42:13+00:00
{"license": "apache-2.0"}
2023-05-15T18:42:58+00:00
168d71a88ea76272d85a2ea529db09b852aebad8
fcdufycv8yivy/random_code
[ "license:cc", "region:us" ]
2023-05-15T18:45:51+00:00
{"license": "cc"}
2023-05-15T18:45:51+00:00
5af6c31ff76f2fca563880b364b0421014d90adf
# Dataset Card for "english_to_pirate" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
c-s-ale/english_to_pirate
[ "region:us" ]
2023-05-15T18:52:03+00:00
{"dataset_info": {"features": [{"name": "english", "dtype": "string"}, {"name": "pirate", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 622.4, "num_examples": 8}, {"name": "test", "num_bytes": 77.8, "num_examples": 1}, {"name": "valid", "num_bytes": 77.8, "num_examples": 1}], "download_size": 5260, "dataset_size": 777.9999999999999}}
2023-05-15T18:52:11+00:00
58ed6828f8d7fd33dbd4aa34d3769e3292172245
# Dataset Card for "mc4_3.1.0_fi_cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Finnish-NLP/mc4_3.1.0_fi_cleaned
[ "region:us" ]
2023-05-15T18:54:32+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "timestamp", "dtype": "timestamp[s]"}, {"name": "url", "dtype": "string"}, {"name": "perplexity_kenlm", "dtype": "int64"}, {"name": "label_identity_attack", "dtype": "float64"}, {"name": "label_insult", "dtype": "float64"}, {"name": "label_obscene", "dtype": "float64"}, {"name": "label_severe_toxicity", "dtype": "float64"}, {"name": "label_threat", "dtype": "float64"}, {"name": "label_toxicity", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 103354369732, "num_examples": 26468761}, {"name": "validation", "num_bytes": 101931416, "num_examples": 26149}], "download_size": 7141130482, "dataset_size": 103456301148}}
2023-05-19T15:20:51+00:00
ffbdd6c1749d15fdc76f265e5f5bcc0215cd086e
# Dataset Card for "fr-crawler-private-mlm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
factored/fr-crawler-private
[ "region:us" ]
2023-05-15T18:55:57+00:00
{"dataset_info": {"features": [{"name": "labels", "dtype": {"class_label": {"names": {"0": "Data Engineer", "1": "Machine Learning Engineer", "2": "Data Analyst", "3": "Data Scientist", "4": "MLOps", "5": "Analytics Engineer", "6": "Software Engineer"}}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 301890.6, "num_examples": 1659}, {"name": "val", "num_bytes": 100630.2, "num_examples": 553}, {"name": "test", "num_bytes": 100630.2, "num_examples": 553}], "download_size": 279647, "dataset_size": 503151.0}}
2023-05-15T19:17:11+00:00
aac5e3bb80900f9bf2b80a9bbfd299ca16c54db0
# Dataset Card for "e6ed0e01" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/e6ed0e01
[ "region:us" ]
2023-05-15T19:23:28+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1336, "dataset_size": 186}}
2023-05-15T19:23:29+00:00
d5322009357ea0c213c3fc945273df19360737a2
# Dataset Card for "resd_ref" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ar4ikov/resd_ref
[ "region:us" ]
2023-05-15T19:34:08+00:00
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "emotion", "dtype": "string"}, {"name": "speech", "dtype": {"audio": {"sampling_rate": 16000}}}], "splits": [{"name": "train", "num_bytes": 430040029.904, "num_examples": 1116}, {"name": "test", "num_bytes": 106255230.0, "num_examples": 280}], "download_size": 467429093, "dataset_size": 536295259.904}}
2023-05-16T11:34:03+00:00
69f89df68b1168d6dad08c79a86b8e226f18c40e
HK83/Anime_Faces
[ "license:afl-3.0", "region:us" ]
2023-05-15T19:51:30+00:00
{"license": "afl-3.0"}
2023-05-15T19:52:40+00:00
4a1e9ad3751487c4b2e086dbae232a97e4ac6535
# Dataset Card for "18ae53b8" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/18ae53b8
[ "region:us" ]
2023-05-15T20:12:44+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 184, "num_examples": 10}], "download_size": 1337, "dataset_size": 184}}
2023-05-15T20:12:46+00:00
72b829eec9fa878338c300e1ac85800fe257667d
winglian/chatlogs-en-cleaned
[ "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "region:us" ]
2023-05-15T20:18:50+00:00
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "chatlogs cleaned (en)"}
2023-05-21T22:54:23+00:00
d25387668c668aee4a8ece57aab28484b2359b3e
## LoRA Description This is LoRA trained on the Kafka character art from Honkai: Star Rail. Enjoy using it! ### CivitAi: https://civitai.com/models/67079/kafka-honkai-star-rail ## Example images ![Demo](https://i.imgur.com/OzMSvg0.png)
Katrg/Kafka-HonkaiStarRail
[ "license:creativeml-openrail-m", "lora", "aiartchan", "stable-diffusion", "art", "region:us" ]
2023-05-15T20:21:55+00:00
{"license": "creativeml-openrail-m", "tags": ["lora", "aiartchan", "stable-diffusion", "art"]}
2023-05-16T20:10:30+00:00
59438971a6cf206fd2b1148f09e9742a3991ed58
# Dataset Card for "restmex23-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
javilonso/restmex23-test
[ "region:us" ]
2023-05-15T20:23:03+00:00
{"dataset_info": {"features": [{"name": "ID", "dtype": "int64"}, {"name": "Title_Review", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43836074, "num_examples": 107863}], "download_size": 27668633, "dataset_size": 43836074}}
2023-05-15T20:23:10+00:00
907795ef7194cadce4e2edcc9d01ff5ba7c2de23
# Dataset Card for all_combined_odia_171K ## Dataset Description - **Homepage: https://www.odiagenai.org/** - **Repository: https://github.com/shantipriyap/OdiaGenAI** - **Point of Contact: Shantipriya Parida, and Sambit Sekhar** ### Dataset Summary This dataset is a mix of Odia instruction sets translated from open-source instruction sets. The Odia instruction sets used are: * dolly-odia-15k * OdiEnCorp_translation_instructions_25k * gpt-teacher-roleplay-odia-3k * Odia_Alpaca_instructions_52k * hardcode_odia_qa_105 In this dataset Odia instruction, input, and output strings are available. ### Supported Tasks and Leaderboards Large Language Model (LLM) ### Languages Odia ## Dataset Structure JSON ### Data Fields output (string) data_source (string) instruction (string) input (string) ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa]. [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg ### Citation Information If you find this repository useful, please consider giving 👏 and citing: ``` @misc{OdiaGenAI, author = {Shantipriya Parida and Sambit Sekhar and Subhadarshi Panda and Soumendra Kumar Sahoo and Swateek Jena and Abhijeet Parida and Arghyadeep Sen and Satya Ranjan Dash and Deepak Kumar Pradhan}, title = {OdiaGenAI: Generative AI and LLM Initiative for the Odia Language}, year = {2023}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/OdiaGenAI}}, } ``` ### Contributions - Shantipriya Parida - Sambit Sekhar
OdiaGenAI/all_combined_odia_171k
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:or", "license:cc-by-nc-sa-4.0", "region:us" ]
2023-05-15T20:28:15+00:00
{"language": ["or"], "license": "cc-by-nc-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "all_combined_odia_171K"}
2023-05-23T07:19:22+00:00
b7e23dee303aa54ebf620fddea5063a343cee993
# Dataset Card for "52473874" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/52473874
[ "region:us" ]
2023-05-15T20:29:41+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1341, "dataset_size": 180}}
2023-05-15T20:29:43+00:00
5aad23e6089ca8a5a1ab0805cdb2aef272cca3df
# Dataset Card for sama-drives-california ![Alt text](https://sama-documentation-assets.s3.amazonaws.com/sama-drives-california/samples/samples.png "Samples") ## Dataset Description - **Homepage:** www.sama.com - **Point of Contact:** [email protected] ### Dataset Summary This is an object detection dataset (bounding boxes and polygons) of **25 136 frames** (848x480 pixels) taken by a dashboard video camera of a car driving in California. The frames were captured at 1 FPS, and hence the entire footage covers over 7 hours of driving. All but 110 frames contain at least one annotated object (25 026) of interest. ## Dataset Structure ### Data Instances The dataset is saved according to the `bdd100k` format described [here](https://doc.bdd100k.com/format.html#segmentation-formats) (no affiliation with Sama). Frames are named according to the original video they are from, along with the sequence index in that video (1-indexed): **videoNumber-frameIndex.jpg** \ (e.g., 099-002.jpg for the second frame of the 99th video) `label:id`s are used to denote unique objects, such as a specific vehicle, throughout an entire video, but not across videos. The first digits of a `label:id` denote what video it is from (e.g., the `id` 53002 comes from video 53). Frames were taken from videos that were recorded in a continuous sequence without any time gap in between videos. However, some videos were not included \ in the final dataset either because they contained sensitive information or because they were part of a long sequence when the car was parked and facing a scene of no interest. The labelling format and different classes supported are described in the section Data Fields below. Sample annotation: ```json { "name": "001-019.jpg", "attributes": {"weather": "Sunny", "timeofday": "Day"}, "labels": [ {"category": "Drivable Space", "attributes": {"occluded": true}, "manualShape": true, "manualAttributes": true, "id": 1001, "poly2d": [{"vertices": [[369, 296], [370, 276], [389, 277], [432, 278], [494, 279], [504, 266], [563, 262], [590, 270], [656, 271], [705, 276], [776, 270], [847, 274], [847, 337], [847, 419], [766, 408], [681, 402], [626, 400], [550, 393], [507, 391], [426, 390], [321, 387], [242, 394], [206, 402], [170, 402], [135, 399], [72, 405], [29, 413], [0, 418], [0, 259], [66, 259], [91, 267], [154, 265], [126, 280], [145, 288], [188, 284], [155, 265], [187, 265], [225, 263], [309, 260], [301, 271], [345, 272], [370, 276], [369, 296], [306, 300], [225, 300], [226, 312], [309, 334], [416, 353], [552, 373], [635, 375], [669, 365], [666, 343], [654, 338], [542, 313]], "closed": true, "filled": true}], "box2d": {"x1": 0, "y1": 259, "x2": 847, "y2": 419}}, {"category": "Vehicles | Truck", "attributes": {"occluded": true}, "manualShape": true, "manualAttributes": true, "id": 1041, "poly2d": [{"vertices": [[708, 247], [692, 247], [688, 251], [687, 258], [687, 265], [709, 265], [714, 265], [713, 255]], "closed": true, "filled": true}], "box2d": {"x1": 687, "y1": 247, "x2": 714, "y2": 265}}, {"category": "Vehicles | Truck", "attributes": {"occluded": true}, "manualShape": true, "manualAttributes": true, "id": 1043, "poly2d": [{"vertices": [[468, 238], [486, 251], [494, 253], [500, 257], [507, 258], [515, 262], [527, 267], [530, 278], [531, 293], [503, 300], [482, 299], [425, 291], [426, 296], [415, 298], [409, 291], [391, 288], [390, 299], [375, 300], [369, 289], [353, 284], [354, 254], [409, 256], [424, 238]], "closed": true, "filled": true}], "box2d": {"x1": 353, "y1": 238, "x2": 531, "y2": 300}}, {"category": "Vehicles | Car", "attributes": {"occluded": true}, "manualShape": true, "manualAttributes": true, "id": 1044, "poly2d": [{"vertices": [[560, 256], [539, 253], [541, 257], [553, 264], [561, 271], [563, 288], [568, 288], [584, 290], [596, 288], [599, 277], [595, 271], [589, 267], [577, 264], [570, 260]], "closed": true, "filled": true}], "box2d": {"x1": 539, "y1": 253, "x2": 599, "y2": 290}}, {"category": "Vehicles | Car", "attributes": {"occluded": true}, "manualShape": true, "manualAttributes": true, "id": 1045, "poly2d": [{"vertices": [[507, 246], [499, 247], [495, 248], [506, 255], [523, 262], [526, 270], [532, 281], [530, 295], [547, 296], [565, 294], [562, 271], [551, 261], [537, 254], [519, 251]], "closed": true, "filled": true}], "box2d": {"x1": 495, "y1": 246, "x2": 565, "y2": 296}}, {"category": "Vehicles | Car", "attributes": {"occluded": false, "drivingConditions": "Light Traffic"}, "manualShape": true, "manualAttributes": true, "id": 1046, "poly2d": [{"vertices": [[30, 249], [14, 249], [9, 256], [8, 262], [10, 271], [13, 271], [13, 269], [24, 269], [24, 271], [30, 271], [32, 268], [36, 268], [38, 271], [41, 269], [41, 263], [40, 256], [37, 252], [34, 250]], "closed": true, "filled": true}], "box2d": {"x1": 8, "y1": 249, "x2": 41, "y2": 271}} ] } ``` ### Data Fields Each frame contains a label for `timeofday` and `weather`. `Dusk`, `Dawn` and `Twilight` all fall in the same `timeofday` category. | timeofday | weather | |:--------------------|:--------| | Day | Sunny | | Night | Cloudy | | Dusk/Dawn/Twilight | Rainy | | | Snowy | | | Other | Bounding boxes are provided for all objects as `box2d`. `Vehicles`, `People` and `Areas` are also identified with closed `Polygons` of the type `poly2d`. `Lanes` are available as `Lines`, that are denoted as open `Polygons` of the type `poly2d`. `Traffic Lights` and `Traffic Signs` are only available as `Bounding Boxes`. | Vehicles (Polygons) | People (Polygons) | Areas (Polygons) | Lanes (Lines) | Traffic (Bounding Boxes) | |:----------------------|:----------------------|:-------------------|:------------------|:--------------------------| | Car | Pedestrians | Drivable Space | Current Lane | Traffic Lights | | Truck | | | Alternate Lane | Traffic Signs | | Van | | | Opposite Lane | | | SUV | | | | | | Bus | | | | | | Other LV | | | | | | Bicycles | | | | | | Motorbikes | | | | | The objects above can each be `occluded` (true) or not (false). `Vehicles` also have a label called `drivingConditions` that denotes the amount of vehicle traffic they are facing. Note that this label is not always present. | drivingConditions (for Vehicles) | |:------------------------------------| | Light Traffic | | Moderate Traffic | | Heavy Traffic | `Lanes` also contain a laneChange label. Note that this label is not always present. | laneChange (for Lanes) | |:---------------------------| | Current | | Alternate | | Opposite | ### Visualize Dataset To visualize the dataset on the [FiftyOne](https://docs.voxel51.com/) app, download and unzip the following [zip file](https://sama-documentation-assets.s3.amazonaws.com/sama-drives-california/zipped/sama-drives-california.zip) (2.3GB). ```python import fiftyone as fo # <dataset_dir>/ # labels.json # data/ # 001-001.jpg # 001-002.jpg # ... name = "sama-drives-california" dataset_dir = "/path/to/dataset" # Create the dataset dataset = fo.Dataset.from_dir( dataset_dir=dataset_dir, dataset_type=fo.types.BDDDataset, name=name ) ``` ### Dataset in Video Format This dataset is also available as a video dataset with [FiftyOne](https://docs.voxel51.com/) style label format. You can download a zipped file of the dataset (videos and fiftyone labels) [here](https://sama-documentation-assets.s3.amazonaws.com/sama-drives-california/zipped/sama-drives-california-videos.zip) (1.1GB). ```python import fiftyone as fo # <video_dataset_dir>/ # frames.json # metadata.json # samples.json # data/ # 001.mp4 # 002.mp4 # ... name = "sama-drives-california-videos" dataset_dir = "/path/to/videos-dataset" # Create the dataset dataset = fo.Dataset.from_dir( dataset_dir=dataset_dir, dataset_type=fo.types.FiftyOneDataset, name=name ) ``` ### Annotations The dataset was annotated by a team of Sama Associates. They were instructed to annotate all objects of the classes described in the section *Data Fields* above with the following details: * Ignore objects under 10 pixels in width or height. * Annotate with a pixel tolerance of 2 pixels. * For motorized vehicles, include the mirrors but do not include the antennas. * For bicycles, include the cyclist. * For motorbikes, include the rider. * For traffic lights, place the bounding box around the light fixture but not the pole. * For traffic signs, do not include the pole or structure. ### Personal and Sensitive Information All personal and sensitive information has been removed. Vehicle license plates and faces are blurred. ### Other Known Limitations Objects of interest that were smaller than 10 pixels in width or height were not annotated. ### Licensing Information (CC BY 4.0) [https://creativecommons.org/licenses/by/4.0/]
SamaAI/sama-drives-california
[ "size_categories:10K<n<100K", "license:cc-by-4.0", "region:us" ]
2023-05-15T20:33:54+00:00
{"license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "dataset_info": {"features": [{"name": "fname", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "label", "struct": [{"name": "attributes", "struct": [{"name": "timeofday", "dtype": "string"}, {"name": "weather", "dtype": "string"}]}, {"name": "labels", "list": [{"name": "attributes", "struct": [{"name": "drivingConditions", "dtype": "string"}, {"name": "laneChange", "dtype": "string"}, {"name": "occluded", "dtype": "bool"}]}, {"name": "box2d", "struct": [{"name": "x1", "dtype": "int64"}, {"name": "x2", "dtype": "int64"}, {"name": "y1", "dtype": "int64"}, {"name": "y2", "dtype": "int64"}]}, {"name": "category", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "manualAttributes", "dtype": "bool"}, {"name": "manualShape", "dtype": "bool"}, {"name": "poly2d", "list": [{"name": "closed", "dtype": "bool"}, {"name": "filled", "dtype": "bool"}, {"name": "vertices", "sequence": {"sequence": "int64"}}]}]}, {"name": "name", "dtype": "string"}]}, {"name": "img", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1088252764.96, "num_examples": 25136}], "download_size": 1025635407, "dataset_size": 1088252764.96}}
2023-06-14T13:58:49+00:00
279e5544aad112a43cae00906854233d5ccccf07
# Dataset Card for "covid_qa_ak_203" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
akufeldt/covid_qa_ak_203
[ "region:us" ]
2023-05-15T21:09:48+00:00
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "document_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "id", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "offset_mapping", "sequence": {"sequence": "int64"}}, {"name": "start_positions", "dtype": "int64"}, {"name": "end_positions", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 62307721, "num_examples": 1417}, {"name": "dev", "num_bytes": 6270706, "num_examples": 203}, {"name": "test", "num_bytes": 15221921, "num_examples": 375}], "download_size": 3725979, "dataset_size": 83800348}}
2023-05-15T21:09:59+00:00
94d0cb2f8a70998e641f4f4c9fc3e5eaca866e5f
# Dataset Card for "audiofeatures2albumcovers" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ramgus/audiofeatures2albumcovers
[ "region:us" ]
2023-05-15T21:15:52+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 118277758.258, "num_examples": 1181}], "download_size": 92359249, "dataset_size": 118277758.258}}
2023-05-15T21:16:30+00:00
65880311c678a39b2c87445c834549b5d12c1e46
# Dataset Card for "kkbox" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pytorch-survival/kkbox
[ "region:us" ]
2023-05-15T21:19:57+00:00
{"dataset_info": {"features": [{"name": "msno", "dtype": "string"}, {"name": "n_prev_churns", "dtype": "float32"}, {"name": "log_days_between_subs", "dtype": "float32"}, {"name": "log_days_since_reg_init", "dtype": "float32"}, {"name": "log_payment_plan_days", "dtype": "float32"}, {"name": "log_plan_list_price", "dtype": "float32"}, {"name": "log_actual_amount_paid", "dtype": "float32"}, {"name": "is_auto_renew", "dtype": "float32"}, {"name": "is_cancel", "dtype": "float32"}, {"name": "city", "dtype": "float64"}, {"name": "gender", "dtype": "string"}, {"name": "registered_via", "dtype": "float64"}, {"name": "age_at_start", "dtype": "float32"}, {"name": "strange_age", "dtype": "float32"}, {"name": "nan_days_since_reg_init", "dtype": "float32"}, {"name": "no_prev_churns", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 236008040, "num_examples": 1786358}], "download_size": 105130610, "dataset_size": 236008040}}
2023-05-15T21:20:15+00:00
f93a49d8a006cce48552ec9c7729cd6313d4225b
# Dataset Card for "c961a6e2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/c961a6e2
[ "region:us" ]
2023-05-15T21:30:43+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1338, "dataset_size": 186}}
2023-05-15T21:30:44+00:00
f4e60af58d0799d87258a3b71826b2147988a1e9
Miuno/My_falling_set
[ "license:cc", "region:us" ]
2023-05-15T21:48:50+00:00
{"license": "cc"}
2023-05-16T00:00:37+00:00
4e1948b6490dd6a172bd69eec2148a9225b86239
pldb/PLDB
[ "license:unlicense", "region:us" ]
2023-05-15T21:49:49+00:00
{"license": "unlicense"}
2023-05-15T21:51:41+00:00
a5772f61e33ef938bcd28df338f2258558d53f22
Downloaded from https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-WebQuery For more details about the dataset collection and usage, please refer to the ACL 2021 paper (https://arxiv.org/abs/2105.13239) and the GitHub repo (https://github.com/Jun-jie-Huang/CoCLR).
gonglinyuan/CoSQA
[ "license:mit", "arxiv:2105.13239", "region:us" ]
2023-05-15T22:55:35+00:00
{"license": "mit"}
2023-05-15T22:57:34+00:00
39481652408123c412b400f50b2eef530d2da1e8
# TyDi-AS2 ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [TyDi-AS2](#tydi-as2) - [Xtr-TyDi-AS2](#xtr-tydi-as2) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Amazon Science](https://www.amazon.science/publications/cross-lingual-knowledge-distillation-for-answer-sentence-selection-in-low-resource-languages) - **Paper:** [Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages](https://aclanthology.org/2023.findings-acl.885/) - **Point of Contact:** [Yoshitomo Matsubara]([email protected]) ### Dataset Summary ***TyDi-AS2*** and ***Xtr-TyDi-AS2*** are multilingual Answer Sentence Selection (AS2) datasets comprising 8 diverse languages, proposed in our paper accepted at ACL 2023 (Findings): [**Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages**](https://aclanthology.org/2023.findings-acl.885/). Both the datasets were created from [TyDi-QA](https://ai.google.com/research/tydiqa), a multilingual question-answering dataset. TyDi-AS2 was created by converting the QA instances in TyDi-QA to AS2 instances (see [Dataset Creation](#dataset-creation) for details). Xtr-TyDi-AS2 was created by translating the non-English TyDi-AS2 instances to English and vise versa. For translations, we used [Amazon Translate](https://aws.amazon.com/translate/). ### Languages #### TyDi-AS2 (original) - `bn`: Bengali - `en`: English - `fi`: Finnish - `id`: Indonesian - `ja`: Japanese - `ko`: Korean - `ru`: Russian - `sw`: Swahili File location: [`jsonl/original/`](https://huggingface.co/datasets/AmazonScience/tydi-as2/tree/main/jsonl/original/) For non-English sets, we also have English-translated samples used for the cross-lingual knowledge distillation (CLKD) experiments in our paper. File location: [`jsonl/x-to-en/`](https://huggingface.co/datasets/AmazonScience/tydi-as2/tree/main/jsonl/x-to-en/) #### Xtr-TyDi-AS2 (translationese) Xtr-TyDi-AS2 (X-translated TyDi-AS2) dataset consists of non-English AS2 instances translated from the English set of TyDi-AS2. - `bn`: Bengali - `fi`: Finnish - `id`: Indonesian - `ja`: Japanese - `ko`: Korean - `ru`: Russian - `sw`: Swahili File location: [`jsonl/en-to-x/`](https://huggingface.co/datasets/AmazonScience/tydi-as2/tree/main/jsonl/en-to-x/) ## Dataset Structure ### Data Instances This is an example instance from the English training split of TyDi-AS2 dataset. ``` { "Question": "When was the Argentine Basketball Federation formed?", "Title": "History of the Argentina national basketball team", "Sentence": "The Argentina national basketball team represents Argentina in basketball international competitions, and is controlled by the Argentine Basketball Federation.", "Label": 0 } ``` For English-translated TyDi-AS2 dataset and Xtr-TyDi-AS2 dataset, the translated instances in JSONL files are listed in the same order of the original (native) instances in the original TyDi-AS2 dataset. For example, the 2nd instance in [`jsonl/x-to-en/en_from_bn-train.jsonl`](jsonl/x-to-en/en_from_bn-train.jsonl) (English-translated from Bengali) corresponds to the 2nd instance in [`jsonl/original/bn-train.jsonl`](jsonl/original/bn-train.jsonl) (Bengali). Similarly, the 2nd instance in [`jsonl/en-to-x/bn_from_en-train.jsonl`](jsonl/en-to-x/bn_from_en-train.jsonl) (Bengali-translated from English) corresponds to the 2nd instance in [`jsonl/original/en-train.jsonl`](jsonl/original/en-train.jsonl) (English). ### Data Fields Each instance (a QA pair) consists of the following fields: - `Question`: Question to be answered (str) - `Title`: Document title (str) - `Sentence`: Answer sentence in the document (str) - `Label`: Label that indicates the answer sentence correctly answers the question (int, 1: correct, 0: incorrect) ### Data Splits | | | **#Questions** | | | | **#Sentences** | | |---------------------|----------:|---------------:|---------:|---|----------:|---------------:|---------:| | | **train** | **dev** | **test** | | **train** | **dev** | **test** | | **Bengali (bn)** | 7,978 | 2,056 | 316 | | 1,376,432 | 351,186 | 37,465 | | **English (en)** | 6,730 | 1,686 | 918 | | 1,643,702 | 420,899 | 249,513 | | **Finnish (fi)** | 10,859 | 2,731 | 1,870 | | 1,567,695 | 408,205 | 298,093 | | **Indonesian (id)** | 9,310 | 2,339 | 1,355 | | 960,270 | 236,076 | 97,057 | | **Japanese (ja)** | 11,848 | 2,981 | 1,504 | | 3,183,037 | 822,654 | 444,106 | | **Korean (ko)** | 7,354 | 1,943 | 1,389 | | 1,558,191 | 392,361 | 199,043 | | **Russian (ru)** | 9,187 | 2,294 | 1,395 | | 3,190,650 | 820,668 | 367,595 | | **Swahili (sw)** | 8,350 | 2,850 | 1,896 | | 1,048,303 | 269,894 | 74,775 | See [our paper](#citation-information) for more details about the statistics of the datasets. ## Dataset Creation ### Source Data The source of TyDi-AS2 dataset is [TyDi QA](https://ai.google.com/research/tydiqa), which is a question answering dataset. ### Annotations #### Annotation process TyDi QA is a QA dataset spanning questions from 11 typologically diverse languages. Each instance comprises a human-generated question, a single Wikipedia document as context, and one or more spans from the document containing the answer. To convert each instance into AS2 instances, we split the context document into sentences and heuristically identify the correct asnwer sentences using the annotated answer spans. To split documents, we use multiple different sentence tokenizers for the diverse languages and omit languages for which we could not find a suitable sentence tokenizer: 1. [bltk](https://github.com/saimoncse19/bltk) for Bengali 2. [blingfire](https://github.com/microsoft/BlingFire) for Swahili, Indonesian, and Korean 3. [pysdb](https://github.com/nipunsadvilkar/pySBD) for English and Russian 4. [nltk](https://www.nltk.org/) for Finnish 5. [Konoha](https://github.com/himkt/konoha) for Japanese #### Who are the annotators? [Shivanshu Gupta](https://huggingface.co/shivanshu) converted TyDi QA to TyDi-AS2. [Yoshitomo Matsubara](https://huggingface.co/yoshitomo-matsubara) translated non-English samples to English and vice versa for Xtr-TyDi-AS2 dataset Since sentence tokenization and identifying answer sentences can introduce errors, we conducted a manual validation of the AS2 datasets. For each language, we randomly selected 50 instances and verified the accuracy of the answer sentences through manual inspection. Our findings revealed that the answer sentences were accurate in 98% of the cases. ## Additional Information ### Dataset Curators Shivanshu Gupta (@shivanshu) ### Licensing Information [CDLA-Permissive-2.0](LICENSE.md) ### Citation Information ```bibtex @inproceedings{gupta2023cross-lingual, title={{Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages}}, author={Gupta, Shivanshu and Matsubara, Yoshitomo and Chadha, Ankit and Moschitti, Alessandro}, booktitle={Findings of the Association for Computational Linguistics: ACL 2023}, pages={14078--14092}, year={2023} } ``` ### Contributions - [Shivanshu Gupta](https://huggingface.co/shivanshu) - [Yoshitomo Matsubara](https://huggingface.co/yoshitomo-matsubara) - Ankit Chadha - Alessandro Moschitti
AmazonScience/tydi-as2
[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:open-domain-qa", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:multilingual", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|tydiqa", "language:bn", "language:en", "language:fi", "language:id", "language:ja", "language:ko", "language:ru", "language:sw", "license:cdla-permissive-2.0", "as2", "answer sentence selection", "text retrieval", "question answering", "region:us" ]
2023-05-15T23:02:00+00:00
{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["bn", "en", "fi", "id", "ja", "ko", "ru", "sw"], "license": "cdla-permissive-2.0", "multilinguality": ["multilingual", "translation"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|tydiqa"], "task_categories": ["question-answering", "text-retrieval"], "task_ids": ["open-domain-qa"], "pretty_name": "tydi-as2", "license_details": "https://huggingface.co/datasets/AmazonScience/tydi-as2/blob/main/LICENSE.md", "tags": ["as2", "answer sentence selection", "text retrieval", "question answering"]}
2023-07-24T16:33:28+00:00
49cc4ceb83259714e032dcbe7ff3a7b018c52efc
# Xtr-WikiQA ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Amazon Science](https://www.amazon.science/publications/cross-lingual-knowledge-distillation-for-answer-sentence-selection-in-low-resource-languages) - **Paper:** [Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages](https://aclanthology.org/2023.findings-acl.885/) - **Point of Contact:** [Yoshitomo Matsubara]([email protected]) ### Dataset Summary ***Xtr-WikiQA*** is an Answer Sentence Selection (AS2) dataset in 9 non-English languages, proposed in our paper accepted at ACL 2023 (Findings): [**Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages**](https://aclanthology.org/2023.findings-acl.885/). This dataset is based on an English AS2 dataset, WikiQA ([Original](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0), [Hugging Face](https://huggingface.co/datasets/wiki_qa)). For translations, we used [Amazon Translate](https://aws.amazon.com/translate/). ### Languages - Arabic (ar) - Spanish (es) - French (fr) - German (de) - Hindi (hi) - Italian (it) - Japanese (ja) - Dutch (nl) - Portuguese (pt) File location: [`tsv/`](https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/tree/main/tsv) ## Dataset Structure ### Data Instances This is an example instance from the Arabic training split of Xtr-WikiQA dataset. ``` { "QuestionID": "Q1", "Question": "كيف تتشكل الكهوف الجليدية؟", "DocumentID": "D1", "DocumentTitle": "كهف جليدي", "SentenceID": "D1-0", "Sentence": "كهف جليدي مغمور جزئيًا على نهر بيريتو مورينو الجليدي.", "Label": 0 } ``` All the translated instances in tsv files are listed in the same order of the original (native) instances in the WikiQA dataset. For example, the 2nd instance in [`tsv/ar-train.tsv`](https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/blob/main/tsv/ar-train.tsv) (Arabic-translated from English) corresponds to the 2nd instance in [`WikiQA-train.tsv`](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0) (English). ### Data Fields Each instance (a QA pair) consists of the following fields: - `QuestionID`: Question ID (str) - `Question`: Question to be answered (str) - `DocumentID`: Document ID (str) - `DocumentTitle`: Document title (str) - `SentenceID`: Answer sentence in the document (str) - `Sentence`: Answer sentence in the document (str) - `Label`: Label that indicates the answer sentence correctly answers the question (int, 1: correct, 0: incorrect) ### Data Splits | | | **#Questions** | | | | **#Sentences** | | |-------------------|------------:|---------------:|---------:|---|----------:|---------------:|---------:| | | **train** | **dev** | **test** | | **train** | **dev** | **test** | | **Each language** | 873 | 126 | 243 | | 8,671 | 1,130 | 2,351 | See [our paper](#citation-information) for more details about the statistics of the datasets. ## Dataset Creation ### Source Data The source of Xtr-WikiQA dataset is [WikiQA](https://msropendata.com/datasets/21032bb1-88bd-4656-9570-3172ae1757f0). ## Additional Information ### Licensing Information [CDLA-Permissive-2.0](LICENSE.md) ### Citation Information ```bibtex @inproceedings{gupta2023cross-lingual, title={{Cross-Lingual Knowledge Distillation for Answer Sentence Selection in Low-Resource Languages}}, author={Gupta, Shivanshu and Matsubara, Yoshitomo and Chadha, Ankit and Moschitti, Alessandro}, booktitle={Findings of the Association for Computational Linguistics: ACL 2023}, pages={14078--14092}, year={2023} } ``` ### Contributions - [Shivanshu Gupta](https://huggingface.co/shivanshu) - [Yoshitomo Matsubara](https://huggingface.co/yoshitomo-matsubara) - Ankit Chadha - Alessandro Moschitti
AmazonScience/xtr-wiki_qa
[ "task_categories:question-answering", "task_categories:text-retrieval", "task_ids:open-domain-qa", "annotations_creators:machine-generated", "language_creators:found", "multilinguality:multilingual", "multilinguality:translation", "size_categories:100K<n<1M", "source_datasets:extended|wiki_qa", "language:ar", "language:es", "language:fr", "language:de", "language:hi", "language:it", "language:ja", "language:nl", "language:pt", "license:cdla-permissive-2.0", "as2", "answer sentence selection", "text retrieval", "question answering", "region:us" ]
2023-05-15T23:03:14+00:00
{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["ar", "es", "fr", "de", "hi", "it", "ja", "nl", "pt"], "license": "cdla-permissive-2.0", "multilinguality": ["multilingual", "translation"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|wiki_qa"], "task_categories": ["question-answering", "text-retrieval"], "task_ids": ["open-domain-qa"], "pretty_name": "xtr-wiki_qa", "license_details": "https://huggingface.co/datasets/AmazonScience/xtr-wiki_qa/blob/main/LICENSE.md", "tags": ["as2", "answer sentence selection", "text retrieval", "question answering"]}
2023-07-24T16:32:38+00:00
79c51681efc888b7a4ddfc62b3609471eb63dfc6
# Dataset Card for "flores200_devtest_translation_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bri25yu/flores200_devtest_translation_pairs
[ "region:us" ]
2023-05-15T23:06:44+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "source_lang", "dtype": "string"}, {"name": "target_lang", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "devtest", "num_bytes": 17945893042, "num_examples": 41908944}], "download_size": 9523785739, "dataset_size": 17945893042}}
2023-05-16T01:47:15+00:00
a29d93d4cd1e020952509b04f8c328772b63645b
# Dataset Card for "summary_seq_label_more_splits" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Astonzzh/summary_seq_label_more_splits
[ "region:us" ]
2023-05-16T00:32:43+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "ids", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "summary", "dtype": "string"}, {"name": "sentences", "sequence": "string"}, {"name": "sentence_labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 9047827.502945902, "num_examples": 8401}, {"name": "test", "num_bytes": 502956.24852704874, "num_examples": 467}, {"name": "validation", "num_bytes": 502956.24852704874, "num_examples": 467}], "download_size": 3881149, "dataset_size": 10053739.999999998}}
2023-05-16T00:32:47+00:00
91a76d0b7626172b7fc5ca3c8ca2bbf2233fbefa
# Dataset Card for "UTK-Face-Revised" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
deedax/UTK-Face-Revised
[ "region:us" ]
2023-05-16T00:45:11+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "age", "dtype": "int64"}, {"name": "gender", "dtype": "string"}, {"name": "race", "dtype": "string"}, {"name": "age_group", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 352669015.125, "num_examples": 7623}, {"name": "valid", "num_bytes": 39348419.0, "num_examples": 846}], "download_size": 391281119, "dataset_size": 392017434.125}}
2023-05-16T01:05:28+00:00
3923b519fd180e689d0961bf3a032ece929742f3
C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https://arxiv.org/abs/2305.08322) for more details. Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/SJTU-LIT/ceval/tree/main#how-to-submit) ### Load the data ```python from datasets import load_dataset dataset=load_dataset(r"ceval/ceval-exam",name="computer_network") print(dataset['val'][0]) # {'id': 0, 'question': '使用位填充方法,以01111110为位首flag,数据为011011111111111111110010,求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ''} ``` More details on loading and using the data are at our [github page](https://github.com/SJTU-LIT/ceval#data). Please cite our paper if you use our dataset. ``` @article{huang2023ceval, title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian}, journal={arXiv preprint arXiv:2305.08322}, year={2023} } ```
ceval/ceval-exam
[ "task_categories:text-classification", "task_categories:multiple-choice", "task_categories:question-answering", "size_categories:10K<n<100K", "language:zh", "license:cc-by-nc-sa-4.0", "arxiv:2305.08322", "region:us" ]
2023-05-16T00:47:44+00:00
{"language": ["zh"], "license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "multiple-choice", "question-answering"], "pretty_name": "C-Eval"}
2023-08-31T13:04:10+00:00
9a8ca752098de66f5cf844ea6c4754f84b4c8e02
zuizui/jenna
[ "region:us" ]
2023-05-16T00:53:22+00:00
{}
2023-05-16T00:53:38+00:00
6154afb6c70f43fc014937cbda381d86cc313e7b
# Dataset Card for "d7749d15" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d7749d15
[ "region:us" ]
2023-05-16T00:57:20+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1341, "dataset_size": 178}}
2023-05-16T00:57:21+00:00
88649ea14c761c2e86a1ff4ccdff9945932c7eb5
# Dataset Card for "celeb-identities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
duyduong9htv/celeb-identities
[ "region:us" ]
2023-05-16T01:13:33+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Ali_Wong", "1": "Bobby_Jindal", "2": "Jennifer_Lawrence", "3": "Kate_Middleton", "4": "Keanu_Reeves", "5": "Usain_Bolt"}}}}], "splits": [{"name": "train", "num_bytes": 1489736.0, "num_examples": 20}], "download_size": 1354522, "dataset_size": 1489736.0}}
2023-05-16T01:13:37+00:00
c8347ccecc281245c66388800d7858fd6a63adb9
# Dataset Card for "62de9313" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/62de9313
[ "region:us" ]
2023-05-16T02:08:23+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1332, "dataset_size": 180}}
2023-05-16T02:08:24+00:00
2e3b520d6df1f04893064c1b3be8faef96086377
# Dataset Card for "google_maps" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MexFoundation/google_maps
[ "region:us" ]
2023-05-16T03:07:10+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "image_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3825218012.0, "num_examples": 7300}], "download_size": 3825269212, "dataset_size": 3825218012.0}}
2023-05-16T03:12:48+00:00
745dfe1c3f1d2c438d1b35d4cfa0b84850d3b236
MMC4-130k-chinese是对MMC4中,抽样了130k左右 simliarty较高的图文pair得到的数据集 Chinese版本是对这里所有的caption进行了翻译。 我们会陆续将更多数据集发布到hf,包括 - [ ] Coco Caption的中文翻译 - [ ] CoQA的中文翻译 - [ ] CNewSum的Embedding数据 - [ ] 增广的开放QA数据 - [x] WizardLM的中文翻译 如果你也在做这些数据集的筹备,欢迎来联系我们,避免重复花钱。 # 骆驼(Luotuo): 开源中文大语言模型 [https://github.com/LC1332/Luotuo-Chinese-LLM](https://github.com/LC1332/Luotuo-Chinese-LLM) 骆驼(Luotuo)项目是由[冷子昂](https://blairleng.github.io) @ 商汤科技, 陈启源 @ 华中师范大学 以及 李鲁鲁 @ 商汤科技 发起的中文大语言模型开源项目,包含了一系列语言模型。 ( 注意: [陈启源](https://qiyuan-chen.github.io/) 正在寻找2024推免导师,欢迎联系 ) 骆驼项目**不是**商汤科技的官方产品。 ## Citation Please cite the repo if you use the data or code in this repo. ``` @misc{alpaca, author={Ziang Leng, Qiyuan Chen and Cheng Li}, title = {Luotuo: An Instruction-following Chinese Language model, LoRA tuning on LLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/LC1332/Luotuo-Chinese-LLM}}, } ```
silk-road/MMC4-130k-chinese-image
[ "task_categories:text-to-image", "task_categories:image-to-text", "size_categories:100K<n<1M", "language:zh", "language:en", "license:cc-by-4.0", "region:us" ]
2023-05-16T03:42:42+00:00
{"language": ["zh", "en"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-to-image", "image-to-text"]}
2023-05-16T03:51:58+00:00
1de695496a69b0ae92e184f26d750908c9c2ac34
ramonpzg/latin_music
[ "license:apache-2.0", "region:us" ]
2023-05-16T04:05:41+00:00
{"license": "apache-2.0"}
2023-05-16T15:42:38+00:00
e0980b58cf9868842b517f3a34e044c28f3c648c
# Dataset Card for "WISE1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
PeterPanTheGenius/WISE1
[ "region:us" ]
2023-05-16T04:07:23+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 118870413.0, "num_examples": 996}], "download_size": 118826137, "dataset_size": 118870413.0}}
2023-05-16T05:11:52+00:00
3d272ba032b2d9bd51c3111b61c93b0000a73385
# Cofacts Archive for Reported Messages and Crowd-Sourced Fact-Check Replies [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1qdE-OMJTi6ZO68J6KdzGdxNdheW4ct6T?usp=sharing) The Cofacts dataset encompasses instant messages that have been reported by users of the [Cofacts chatbot](https://line.me/R/ti/p/@cofacts) and the replies provided by the [Cofacts crowd-sourced fact-checking community](https://www.facebook.com/groups/cofacts/). ## Attribution to the Community This dataset is a result of contributions from both Cofacts LINE chatbot users and the community fact checkers. To appropriately attribute their efforts, please adhere to the rules outlined in the [Cofacts 真的假的 資料使用者條款 (Cofacts Data User Agreement)](https://github.com/cofacts/opendata/blob/master/LEGAL.md). Unless stated otherwise, when redistributing Cofacts data outside the LINE application, the attribution specified by the Cofacts Working Group is as follows: > This data by Cofacts message reporting chatbot and crowd-sourced fact-checking community is licensed under CC BY-SA 4.0. To provide more info, please visit Cofacts LINE bot https://line.me/ti/p/@cofacts 除非以其他方式議定,否則 Cofacts 真的假的工作小組,針對在 LINE 之外的地方散布的 Cofacts 所提供資料,所指定的中文顯名聲明為: > 本編輯資料取自「Cofacts 真的假的」訊息回報機器人與查證協作社群,採 CC BY-SA 4.0 授權提供。若欲補充資訊請訪問 Cofacts LINE bot https://line.me/ti/p/@cofacts For more detailed information, please refer to [Cofacts 真的假的 資料使用者條款](https://github.com/cofacts/opendata/blob/master/LEGAL.md). ## How to Access Cofacts Data To access Cofacts data, you should first register on Hugging Face and accept the Cofacts Data User Agreement. Afterward, you can preview the data on the Hugging Face website. You can access Cofacts data through the following methods: 1. Load `cofacts/line-msg-fact-check-tw` with Hugging Face's `load_dataset('Cofacts/line-msg-fact-check-tw', TABLE_NAME)`. 2. Download individual zipped CSV files in the `Files` tab on the Hugging Face website. If you plan to process the data using Python, `load_dataset()` is the simpler solution. Please refer to [Example on Google Colab](https://colab.research.google.com/drive/1qdE-OMJTi6ZO68J6KdzGdxNdheW4ct6T?usp=sharing) to get started. ## Data Formats Cofacts data comprises multiple normalized tables, with some tables containing foreign keys to other tables' IDs. If you have manually downloaded the data, the tables are distributed as zipped CSV files. These files use `\n` as the line terminator, and quotes are used around multi-line contents. The [`csv-stringify`](https://www.npmjs.com/package/csv-stringify) library is employed to perform escaping and handle quotes and multi-line contents. ### Fields in All Tables * `userIdsha` (string) Hashed user identifier. * `appId` (string) Possible values include: * `LEGACY_APP`: Articles collected before 2017-03. * `RUMORS_LINE_BOT`: Articles collected with the current LINE bot client after 2017-03. These two fields together uniquely identify a user across different CSV files. For example, if one row (reply) in `replies.csv` and another row (feedback) in `article_reply_feedbacks.csv` have identical `userIdsha` and `appId`, it indicates that the reply and the feedback were submitted by the same user. Also, these fields are commonly seen in multiple tables: * `status`: The current visibility of this document. Possible values include: * `NORMAL`: The document is normally visible. * `DELETED`: The document is deleted by its author. For some entities (tables), deletion is not implemented, and thus does not have such value. * `BLOCKED`: The document is hidden by Cofacts Working Group. These document are from a blocked user, with `blockedReason` pointing to announcements in [Cofacts Takedown Announcements](https://github.com/cofacts/takedowns). ## Tables and their fields ### `articles` The instant messages LINE bot users submitted into the database. | Field | Data type | Description | | ----------------------- | -------- | ---- | | `id` | String | | | `articleType` | Enum string | `TEXT`, `IMAGE`, `VIDEO` or `AUDIO`. | | `status` | Enum string | `NORMAL` or `BLOCKED`. | | `text` | Text | The instant message text | | `normalArticleReplyCount` | Integer | The number of replies are associated to this article, excluding the deleted reply associations. | | `createdAt` | ISO time string | When the article is submitted to the database. | | `updatedAt` | ISO time string | Preserved, currently identical to `createdAt` | | `lastRequestedAt` | ISO time string | The submission time of the last `reply_request` is sent on the article, before the article is replied. | | `userIdsha256` | String | Author of the article.| | `appId` | String | | | `references` | Enum string | Where the message is from. Currently the only possible value is `LINE`. | ### `article_hyperlinks` Parsed hyperlink contents in each instant messages, parsed using [cofacts/url-resolver](https://github.com/cofacts/url-resolver/). The data is used in Cofacts system for indexing and retrieving messages. | Field | Data type | Description | | ---------------- | -------- | ---- | | `articleId` | String | | | `url` | String | The URL string detected in article | | `normalizedUrl` | String | Canonical URL after normalization process including unfolding shortened URLs | | `title` | String | Title of the scrapped web content | Note: Scrapped contents do not belong to Cofacts and are redistributed under research purposes. The scrapping mechanism is not reliable either. Researchers may need to implement their own scrapper if content is important in their research. ### `article_categories` Categories linked to this article. | Field | Data type | Description | | ---------------- | ---------- | ---- | | `articleId` | String | | | `categoryId` | String | | `aiConfidence` | Number | Confidence level by AI marking this category. Empty for crowd-sourced labels. | | `aiModel` . | String | Name of the AI model marking this cateogry. Empty for crowd-sourced labels. | | `userIdsha256` | String | The person that connected article and category. | | `appId` . | String | | | `negativeFeedbackCount` | Integer | Number of `article_category_feedbacks` that has score `-1` | | `positiveFeedbackCount` | Integer | Number of `article_category_feedbacks` that has score `1` | | `status` | Enum string | `NORMAL`: The category and article are connected. `DELETED`: The category does not connect to the article anymore. | | `createdAt` | ISO time string | The time when the reply is connected to the article | | `updatedAt` | ISO time string | The latest date when the category's status is updated | ### `categories` | Field | Data type | Description | | ------------- | --------- | ----------- | | `id` | String | | | `title` | String | Name of the category | | `description` | Text | Definition of the category | | `createdAt` | ISO time string | | | `updatedAt` | ISO time string | | ### `article_replies` Articles and replies are in has-and-belongs-to-many relationship. That is, an article can have multiple replies, and a reply can be connected to multiple similar articles. `article_replies` is the "join table" between `articles` and `replies`, bringing `articleId` and `replyId` together, along with other useful properties related to this connection between an article and a reply. One pair of `articleId`, `replyId` will map to exactly one `article_reply`. | Field | Data type | Description | | --------------------- | -------- | - | | `articleId` | String | Relates to `id` field of `articles` | | `replyId` | String | Relates to `id` field of `replies` | | `userId` | String | The user connecting the reply with the article | | `negativeFeedbackCount` | Integer | Number of `article_reply_feedbacks` that has score `-1` | | `positiveFeedbackCount` | Integer | Number of `article_reply_feedbacks` that has score `1` | | `replyType` | Enum string | Duplicated from `replies`'s type. | | `appId` | String | | | `status` | Enum string | `NORMAL`: The reply and article are connected. `DELETED`: The reply does not connect to the article anymore. `BLOCKED`: It comes from a blocked user. | | `createdAt` | ISO time string | The time when the reply is connected to the article | | `updatedAt` | ISO time string | The latest date when the reply's status is updated | ### `replies` Editor's reply to the article. | Field | Data type | Description | | --------- | -------- | - | | `id` | String | | | `type` | Enum string | Type of the reply chosen by the editor. `RUMOR`: The article contains rumor. `NOT_RUMOR`: The article contains fact. `OPINIONATED`: The article contains personal opinions. `NOT_ARTICLE`: The article should not be processed by Cofacts. | | `reference` | Text | For `RUMOR` and `NOT_RUMOR` replies: The reference to support the chosen `type` and `text`. For `OPINIONATED` replies: References containing different perspectives from the `article`. For `NOT_ARTICLE`: empty string. | | `userId` | String | The editor that authored this reply. | | `appId` | String | | | `text` | Text | Reply text writtern by the editor | | `createdAt` | ISO Time string | When the reply is written | ### `reply_hyperlinks` Parsed hyperlink contents in reply text and references, parsed using [cofacts/url-resolver](https://github.com/cofacts/url-resolver/). The data is used in Cofacts system for URL previews. | Field | Data type | Description | | ---------------- | -------- | ---- | | `replyId` | String | | | `url` | String | The URL string detected in article | | `normalizedUrl` | String | Canonical URL after normalization process including unfolding shortened URLs | | `title` | String | Title of the scrapped web content | Note: Scrapped contents do not belong to Cofacts and are redistributed under research purposes. The scrapping mechanism implementation is not reliable either. Researchers may need to implement their own scrapper if content is important in their research. ### `reply_requests` Before an article is replied, users may submit `reply_requests` to indicate that they want this article to be answered. When an article is first submitted to the article, an reply request is also created. Any further queries to the same article submits new `reply_requests`. An user can only submit one reply request to an article. | Field | Data type | Description | | --------- | -------- | - | | `articleId` | String | The target of the request | | `reason` | Text | The reason why the user wants to submit this reply request | | `status` | Enum string | `NORMAL` or `BLOCKED`. | | `positiveFeedbackCount` | Text | Number of editors think the reason is reasonable | | `negativeFeedbackCount` | Text | Number of editors think the reason is nonsense | | `createdAt` | ISO Time string | When the reply request is issued | ### `article_reply_feedbacks` Editors and LINE bot users can express if a reply is useful by submitting `article_reply_feedbacks` toward a `article_reply` with score `1` or `-1`. The feedback is actually submitted toward an `article_reply`, the connection between an article and a reply. This is because a reply can be connected to multiple articles. A reply that makes sense in one article does not necessarily mean that it is useful in answering another article. Therefore, the feedback count for a reply connecting to different articles are counted separately. | Field | Data type | Description | | --------- | -------- | - | | `articleId` | String | Relates to `articleId` of the target `article_reply` | | `replyId` | String | Relates to `replyId` of the target `article_reply` | | `score` | Integer | `1`: Useful. `-1`: Not useful. | | `comment` | Text | Why the user chooses such score for this article reply | | `status` | Enum string | `NORMAL` or `BLOCKED`. | | `createdAt` | ISO Time string | When the feedback is submitted | ### `analytics` Usage (visit / show) statistics of website and Cofacts LINE bot. LINE bot data starts from April 2nd, 2018; website data starts from May 3rd, 2017. | Field | Data type | Description | | ----------- | --------------- | ----------- | | `type` | Enum string | Either `article` or `reply` | | `docId` | String | Article ID or Reply ID that is being visited / shown | | `date` | ISO Time string | The date of usage, represented by start of the day (0:00:00+08:00) | | `lineUser` | Integer | The number of LINE users who inspected this article / reply in Cofacts LINE bot in this date. May be empty if no such users | | `lineVisit` | Integer | The number of times this article / reply is inspected in Cofacts LINE bot in this date. May be empty if no visits | | `webUser` | Integer | The number of web users who visited this article page (`/article/<docId>`) / reply page (`/reply/<docId>`) in Cofacts website in this date. May be empty if no such users | | `webVisit` | Integer | The number of page views of this article page (`/article/<docId>`) / reply page (`/reply/<docId>`) in Cofacts website in this date. May be empty if no page views | ### `anonymized_usrs` The users of Cofacts, including Cofacts chatbot and website users. | Field | Data type | Description | | ----------- | --------------- | ----------- | | `userIdsha256` | String | The ID that is used in other tables to denote the creator of the entity. | | `appId` | String | Where this user account is registered. `RUMORS_LINE_BOT` is Cofacts official LINE account. Registered user on Cofacts website has empty `appId`. | | `createdAt` | ISO Time string | The initial registration date for the user. | | `lastActiveAt` | ISO Time string | The last date the account is active. | | `blockedReason` | String | If exists, all submission from the user is hidden by Cofacts WG. This field contains the announcement to the reason why Cofacts WG blocks such user. | ## ⚠ [NOTICE] Caveats of using this data ⚠ The methodology we use to collect these data (i.e. [how Cofacts works](https://beta.hackfoldr.org/cofacts/https%253A%252F%252Fhackmd.io%252Fs%252FBJSdbUMpZ)) could have some impact on the data credibility. ![How cofacts work](https://i.imgur.com/e3Awc50.png) Please keep in mind that all data in this dataset are user-generated, thus is not free from noise and sampling bias coming from these sources: - The distribution Cofacts' users may not reflect the real distribution of all LINE users in Taiwan. - Users may not use Cofacts in the same way we want them to be. Some `articles` may not be actual messages circulating in LINE network. - `replies` may contain factual error. All replies should be merely regarded as "responses to the original message (`article`) to provide different point of view". They are neither the "truth" nor the editor's personal opinion. - There may also exist malicious users sending garbage `articles` into the database. [(Previous incident reports)](https://hackmd.io/@cofacts/incidents) - The program to collect data and to generate dataset may contain error. The dataset may be inaccurate systematically in this way. Lastly, the dataset is provided without warrenty. THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.
Cofacts/line-msg-fact-check-tw
[ "task_categories:text-classification", "task_categories:question-answering", "size_categories:100K<n<1M", "language:zh", "license:cc-by-sa-4.0", "fact-checking", "crowd-sourcing", "region:us" ]
2023-05-16T04:09:10+00:00
{"language": ["zh"], "license": "cc-by-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification", "question-answering"], "pretty_name": "Cofacts archive for reported messages and crowd-sourced fact-check replies", "tags": ["fact-checking", "crowd-sourcing"], "extra_gated_prompt": "To access this repository, you agree to follow the [Cofacts Data User Agreement](https://github.com/cofacts/opendata/blob/master/LEGAL.md). This is vital to sustain a crowd-sourced database like Cofacts to attribute the fact-checking community that contributed to this dataset.\n\u6b32\u5b58\u53d6\u6b64\u8cc7\u6599\u96c6\uff0c\u9700\u540c\u610f[Cofacts \u771f\u7684\u5047\u7684 \u8cc7\u6599\u4f7f\u7528\u8005\u689d\u6b3e](https://github.com/cofacts/opendata/blob/master/LEGAL.md)\u3002 \u5f70\u986f\u67e5\u6838\u793e\u7fa4\u5c0d\u6b64\u8cc7\u6599\u96c6\u4e4b\u8ca2\u737b\uff0c\u5c0d\u5354\u4f5c\u578b\u8cc7\u6599\u5eab\u5982 Cofacts \u7684\u6c38\u7e8c\u767c\u5c55\u81f3\u95dc\u91cd\u8981\u3002\nIt would be great if you share with us who you are and your planned usage of the Cofacts data. Your cooperation is greatly appreciated. If you have no specific details to share with us, please simply enter \"n/a.\"\n\u82e5\u65b9\u4fbf\u7684\u8a71\uff0c\u5e0c\u671b\u60a8\u53ef\u4ee5\u8207 Cofacts \u5de5\u4f5c\u5c0f\u7d44\u5206\u4eab\u60a8\u7684\u55ae\u4f4d\u4ee5\u53ca\u9810\u8a08\u6703\u600e\u9ebc\u904b\u7528\u9019\u500b\u8cc7\u6599\uff0c\u611f\u8b1d\u60a8\uff01\u82e5\u4e0d\u65b9\u4fbf\uff0c\u53ef\u8f38\u5165\u300cn/a\u300d\u3002", "extra_gated_fields": {"I agree to follow the Data User Agreement and promise to attribute Cofacts as specified \u6211\u540c\u610f\u9075\u5b88\u8cc7\u6599\u4f7f\u7528\u8005\u689d\u6b3e\u4e26\u627f\u8afe\u6309\u898f\u5b9a\u5f70\u986f Cofacts": "checkbox", "Anything to share with us \u6709\u4ec0\u9ebc\u60f3\u8981\u8207\u6211\u5011\u5206\u4eab\u7684\u55ce": "text"}, "configs": [{"config_name": "analytics", "data_files": "analytics.csv.zip"}, {"config_name": "article_categories", "data_files": "article_categories.csv.zip"}, {"config_name": "article_hyperlinks", "data_files": "article_hyperlinks.csv.zip", "lineterminator": "\n"}, {"config_name": "article_replies", "data_files": "article_replies.csv.zip"}, {"config_name": "article_reply_feedbacks", "data_files": "article_reply_feedbacks.csv.zip", "lineterminator": "\n"}, {"config_name": "articles", "data_files": "articles.csv.zip", "lineterminator": "\n", "default": true}, {"config_name": "categories", "data_files": "categories.csv.zip", "lineterminator": "\n"}, {"config_name": "replies", "data_files": "replies.csv.zip", "lineterminator": "\n"}, {"config_name": "reply_hyperlinks", "data_files": "reply_hyperlinks.csv.zip", "lineterminator": "\n"}, {"config_name": "reply_requests", "data_files": "reply_requests.csv.zip", "lineterminator": "\n"}, {"config_name": "anonymized_users", "data_files": "anonymized_users.csv.zip", "lineterminator": "\n"}]}
2024-02-01T09:59:00+00:00
fae65f87edaab285740cdffe1a9a0330f1a03ba7
# Dataset Card for "second-model-our-captions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
unlimitedbananas/second-model-our-captions
[ "region:us" ]
2023-05-16T04:24:58+00:00
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "video-id", "dtype": "int64"}, {"name": "fold-ind", "dtype": "int64"}, {"name": "startphrase", "dtype": "string"}, {"name": "sent1", "dtype": "string"}, {"name": "sent2", "dtype": "string"}, {"name": "gold-source", "dtype": "string"}, {"name": "ending0", "dtype": "string"}, {"name": "ending1", "dtype": "string"}, {"name": "ending2", "dtype": "string"}, {"name": "ending3", "dtype": "string"}, {"name": "ending4", "dtype": "string"}, {"name": "ending5", "dtype": "string"}, {"name": "ending6", "dtype": "string"}, {"name": "ending7", "dtype": "string"}, {"name": "ending8", "dtype": "string"}, {"name": "ending9", "dtype": "string"}, {"name": "ending10", "dtype": "string"}, {"name": "ending11", "dtype": "string"}, {"name": "ending12", "dtype": "string"}, {"name": "ending13", "dtype": "string"}, {"name": "ending14", "dtype": "string"}, {"name": "ending15", "dtype": "string"}, {"name": "ending16", "dtype": "string"}, {"name": "ending17", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1368126, "num_examples": 2004}], "download_size": 383393, "dataset_size": 1368126}}
2023-05-16T04:24:59+00:00
0ec6ca7e6871a3e47084a9c2d6b79bbf661dfc11
SIAKAM/jubao_finetune
[ "license:openrail", "region:us" ]
2023-05-16T04:27:10+00:00
{"license": "openrail"}
2023-05-16T06:16:49+00:00
d0bbe7338bdc1ae82599b4852ad6da31df86c84c
hwchalmers/MomentumII
[ "license:mit", "region:us" ]
2023-05-16T04:29:54+00:00
{"license": "mit"}
2023-05-16T04:30:35+00:00
263ed39a474f17be8b3e6d12e8bb2099dc9821dd
# Dataset Card for "deepfashion_with_captions_blowout" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lirus18/deepfashion_with_captions_blowout
[ "region:us" ]
2023-05-16T04:46:03+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "openpose", "dtype": "image"}, {"name": "cloth", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4597369002.709, "num_examples": 13679}], "download_size": 4429889834, "dataset_size": 4597369002.709}}
2023-05-16T04:47:40+00:00
cb68db239d791d032079bc56fe44f96946c0e19c
# Dataset Card for "wxcutout_controlnet_1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
annuo/wxcutout_controlnet_1k
[ "region:us" ]
2023-05-16T04:55:08+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "mask_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 674343836.512, "num_examples": 1048}], "download_size": 633639704, "dataset_size": 674343836.512}}
2023-05-16T05:13:15+00:00
9a2dc206b29883ee428f64438a2cacc32cf1ce55
This dataset is translated from ShareGPT in Chinese. --- license: apache-2.0 ---
FreedomIntelligence/ShareGPT-CN
[ "region:us" ]
2023-05-16T05:31:53+00:00
{}
2023-05-16T06:08:24+00:00
7d90a04899cbe4a418513c157c7b38c24e0cf25d
# Dataset Card for Mozilla RegretsReporter Public Data ## Dataset Description - **Homepage: https://foundation.mozilla.org/en/youtube/** - **Repository: https://github.com/mozilla-extensions/regrets-reporter** - **Paper: https://foundation.mozilla.org/en/youtube/user-controls/** - **Point of Contact: [email protected]** ### Dataset Summary #### RegretsReporter Data This data set card describes the public data sets made available based on [Mozilla’s RegretsReporter research](https://foundation.mozilla.org/en/youtube/) as well as the [Viu Política research](https://en.vero.org.br/projetos/viu-politica) from the University of Exeter and Vero Instituto. This data was collected from participants in Mozilla’s RegretsReporter studies. Participants installed a web extension to participate in each study. In the case of the first study, data was collected from all participants that installed the extension. In the second, data was only collected from participants that positively opted in to experiment participation after installing the extension. #### Viu Politica Data This data was collected from the Viu Política research project. Data was only collected from participants that positively opted in to experiment participation after installing the extension. The videos included are predominantly in Portuguese. The extension UX was available in Portuguese only, and promotional materials about the study were released in Brazilian Portuguese, for a Brazilian audience. For this study, data includes “tagged videos”, “recommendations”, and “collected videos”: - Tagged videos are videos that study participants considered to contain political content. They are videos for which the participant pressed the “Viu Politica?” (“See Politics?”, loosely translated) button overlayed on the video player or thumbnail. - Recommendations are videos recommended to our participants by YouTube, on the sidebar of the video player page, when the participant is watching a video they tagged as political. Only videos loaded in the browser are included, so the “infinite scroll” of the sidebar is only included to the extent that it is loaded, which will depend on participant scrolling behavior. - Collected videos are videos manually identified on YouTube using a selection of keywords relevant to the 2022 Brazilian elections. These videos were used in the research project to support the two datasets above, and provide further coverage of the videos posted on YouTube during the electoral period. - The full transcripts of all tagged, recommended, and collected videos are also included, as a separate dataset. ### Languages The RegretsReporter videos included are predominantly English, but include over 100 different languages, based on our automatic classification. The extension UX was available in English only, but the extension was still used by speakers of many other languages. The promotional materials about the studies were released in English, Dutch, French, German, Spanish and Brazilian Portuguese. The Viu Politica videos are primarily in Portuguese and that study was exclusively in Portuguese. ## Dataset Structure Data includes “regrets” and “recommendations”: - Regrets are videos that our study participants considered undesirable in some sense: - In the first study, they are videos for which the participant pressed the “Report Regret” toolbar button. - In the second study, they are videos for which the participant pressed the “Stop Recommending” button overlayed on the video player or thumbnail. - Recommendations are videos recommended to our participants by YouTube, either on the front YouTube page, or the sidebar of the video player page. Only videos loaded in the browser are included, so the “infinite scroll” of the sidebar is only included to the extent that it is loaded, which will depend on participant scrolling behavior. ### Data Fields #### RegretsReporter 1: regrets Number of rows: 4,760 Total logical bytes: 12.03 MB The data contains the following fields: - submission_date - Date of regret - submission_country - Country from which regret was sent according to IPGeo lookup. - regret_video_id - YouTube video ID - regret_video_title - Title of video - regret_video_description - Description of video (from YouTube) - regret_video_view_count - View count of video at time of regret - video_post_date - Posting date of video #### RegretsReporter 2 (user control study): regrets Number of rows: 20,633 Total logical bytes: 22 MB The data contains the following fields: - submission_date - Date of regret - submission_country - Country from which regret was sent according to IPGeo lookup. - regret_video_id - YouTube video ID - regret_video_title - Title of video - regret_video_channel - Video Channel (canonicalized) - regret_video_description - Description of video (from YouTube) - regret_video_view_count - View count of video at time of regret #### RegretsReporter 2 (user control study): recommendations Number of rows: 96,001,836 Total logical bytes: 115.72 GB The data contains the following fields: - submission_date - Date of recommendation - submission_country - Country from which report was sent according to IPGeo lookup. - recommendation_video_id - YouTube video ID - recommendation_video_title - Title of video - recommendation_video_channel - Video Channel (canonicalized) - recommendation_video_description - Description of video (from YouTube) - recommendation_video_view_count - View count of video at time of regret #### Viu Política: tagged videos Number of rows: 1,248 Total logical bytes: 807.61 KB The data contains the following fields: - tagged_video_id - YouTube video ID - submission_date - Date of video tagging - video_title - Title of video - view_count - View count of video at time of tagging - video_like_count - Like count of video at time of tagging - video_channel - Name of the YouTube channel where the video was posted - video_channel_link - Link/ID of the YouTube channel where the video was posted - video_full_description - YouTube description of tagged video - video_thumbnail - Link to the thumbnail of the tagged video #### Viu Política: recommended videos Number of rows: 294,697 Total logical bytes: 245.31 MB The data contains the following fields: - recommended_video_id - YouTube video ID of recommended video - submission_date - Date of video tagging - recommended_video_title - Title of recommended video - recommended_view_count - View count of recommended video at time of tagging - recommended_video_like_count - Like count of recommended video at time of tagging - recommended_video_channel - Name of the YouTube channel where the recommended video was posted - recommended_video_channel_link - Link/ID of the YouTube channel where the recommended video was posted - recommended_video_full_description - YouTube description of recommended video - recommended_video_thumbnail - Link to the thumbnail of the recommended video #### Viu Política: collected videos Number of rows: 23,245 Total logical bytes: 11.59 MB The data contains the following fields: - collected_video_id - YouTube video ID of collected video - collected_video_title - Title of collected video - collected_view_count - View count of collected video at time of tagging - collected_video_like_count - Like count of collected video at time of tagging - collected_video_channel - Name of the YouTube channel where the collected video was posted - collected_video_channel_link - Link/ID of the YouTube channel where the collected video was posted - collected_video_full_description - YouTube description of collected video - collected_video_thumbnail - Link to the thumbnail of the collected video #### Viu Política: video transcriptions Total number of .txt files containing video transcriptions: 33,054 Total logical bytes: 735.62 MB ## Caveats ### RegretsReporter Data - Our participants are not representative of YouTube users in general: - We promoted the study to the Mozilla network, which has a particular bias, although some paid promotion was employed and, in the first study, we did specifically target demographics that are underrepresented in our community. - All participants were aware their data would be collected during the study, which could have influenced their behavior. - The UX of the extensions was only available in English, which probably biased our respondents towards English-speakers, although it appears that extensions were still used by some non-English-speakers. - The extension was only available for the desktop versions of Firefox and Chrome. - Regrets are subjective and we have not systematically filtered inauthentic use. - Recommendations are only recorded and regrets are only possible on the desktop device for which the extension was installed. - Country data is based on ipgeo lookup and may have systematic errors. It is occasionally missing when lookup fails - The video metadata (title, channel, etc.) is often missing as it was only acquired for a subset of the data. ### Viu Politica Data - The participants are not representative of YouTube users in general: - The promotion of our study relied on digital influencers and researchers, press coverage, construction of a personalized landing page, and social media promotion, with a focus on Twitter and Instagram, as well as through WhatsApp and email – all of which will inevitably have a particular bias when it comes to the users who respond to it. - All participants were aware their data would be collected during the study, which could have influenced their behavior. - The UX of the extension was only available in Brazilian Portuguese, and advertised to Brazilian audiences. - The extension was only available for the desktop versions of Firefox and Chrome. - Whether a given video is political is a subjective decision and we have not systematically filtered inauthentic use.
mozilla-foundation/youtube_regrets
[ "license:cc0-1.0", "region:us" ]
2023-05-16T05:50:36+00:00
{"license": "cc0-1.0"}
2023-05-16T10:16:40+00:00
7c839e0f19e25276ff43972e73b9dff77dfab689
# Dataset Card for "ufpr-amr-donut" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Chaymaa/ufpr-amr-donut
[ "region:us" ]
2023-05-16T06:10:41+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 6570806.0, "num_examples": 5}, {"name": "train", "num_bytes": 10018278.0, "num_examples": 7}, {"name": "validation", "num_bytes": 4405801.0, "num_examples": 3}], "download_size": 20792209, "dataset_size": 20994885.0}}
2023-05-16T06:11:17+00:00
5cd3c4cb50557d4321e3cb39acb6e5ebbd917438
aimingter/model-2-2.5D
[ "license:openrail", "region:us" ]
2023-05-16T06:22:10+00:00
{"license": "openrail"}
2023-05-16T06:22:10+00:00
351000374df2f76538ffc1aafd9b81b9774a22bc
# Dataset Card for "albumcoversongtitle" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ramgus/albumcoversongtitle
[ "region:us" ]
2023-05-16T06:24:21+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 118136535.258, "num_examples": 1181}], "download_size": 92338136, "dataset_size": 118136535.258}}
2023-05-16T06:24:51+00:00
a17ea46b07967405d29c8915f2d0752998675a7d
Fuckoff12/Excelll
[ "license:bsd", "region:us" ]
2023-05-16T06:26:54+00:00
{"license": "bsd"}
2023-05-16T06:26:54+00:00
a659fd58828ccf07888073a7e18a07063d65c5ab
Hemanth-thunder/en_ta
[ "size_categories:10K<n<100K", "language:ta", "language:en", "license:mit", "region:us" ]
2023-05-16T06:45:59+00:00
{"language": ["ta", "en"], "license": "mit", "size_categories": ["10K<n<100K"]}
2023-08-12T05:58:11+00:00
ff958a6b21b9b286245f8f94062a88027194f28a
This dataset used by the example [inference script](https://github.com/NVIDIA/NeMo/blob/stable/examples/nlp/spellchecking_asr_customization/run_infer.sh) for [SpellMapper](https://arxiv.org/abs/2306.02317) model.
bene-ges/spellmapper_en_evaluation
[ "language:en", "license:cc-by-4.0", "arxiv:2306.02317", "region:us" ]
2023-05-16T06:56:42+00:00
{"language": ["en"], "license": "cc-by-4.0"}
2023-06-06T13:42:20+00:00
2ad750fa2ac72fb9e2b92ef6955990e3a4b2eead
alexwww94/datasets-for-simcse
[ "region:us" ]
2023-05-16T07:01:53+00:00
{}
2023-05-16T07:51:32+00:00
f9080eb8f9223440366092de3156757998949cb2
# Dataset Card for "diffusion.8.instruct_pix2pix" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
lansinuote/diffusion.8.instruct_pix2pix
[ "region:us" ]
2023-05-16T07:18:58+00:00
{"dataset_info": {"features": [{"name": "input", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "output", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 416880509.0, "num_examples": 1000}], "download_size": 416898966, "dataset_size": 416880509.0}}
2023-05-16T09:00:52+00:00
ac4268addebfbd0eddd8a0cb07109f394122dd58
# Dataset Card for "common_voice_13_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GiorgiSekhniashvili/common_voice_13_0
[ "region:us" ]
2023-05-16T07:23:08+00:00
{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "input_length", "dtype": "float64"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 6134418816, "num_examples": 6379}, {"name": "validation", "num_bytes": 4254421264, "num_examples": 4424}], "download_size": 2008703225, "dataset_size": 10388840080}}
2023-05-16T07:35:00+00:00
b779b97c9ec953a6a2f826881ca0a7509c03710a
rajc111/xpertreviews
[ "license:gpl-3.0", "region:us" ]
2023-05-16T07:36:41+00:00
{"license": "gpl-3.0"}
2023-05-16T07:42:23+00:00
04ec7f9e43e5ef5e7f805d67b967ff3d6024cf0c
# Dataset Card for "turkishReviews-ds-mini" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bosnakdev/turkishReviews-ds-mini
[ "region:us" ]
2023-05-16T07:37:04+00:00
{"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1252876.2642514652, "num_examples": 3378}, {"name": "validation", "num_bytes": 139455.7357485349, "num_examples": 376}], "download_size": 0, "dataset_size": 1392332.0}}
2023-05-16T08:04:42+00:00
ce086af501c0eed3f9dd5d6939cdbbca3a386930
# Vercuna Dataset (Verus - Vicuna + LLaVa) This is the Alpha version of the dataset
ShreyasBrill/Vercuna-Dataset
[ "license:mit", "region:us" ]
2023-05-16T07:43:19+00:00
{"license": "mit"}
2023-05-18T15:48:53+00:00
4070dd84432df73ce2b4ef5d06b0c190907797bd
aimingter/vae
[ "license:openrail", "region:us" ]
2023-05-16T08:05:36+00:00
{"license": "openrail"}
2023-05-16T08:17:26+00:00
e24764f1fb58c26b5f622157644f2e5fe77e5b01
# Dataset Card for MMLU ## 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 - **Repository**: https://github.com/hendrycks/test - **Paper**: https://arxiv.org/abs/2009.03300 ### Dataset Summary [Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021). This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions'] ### Supported Tasks and Leaderboards | Model | Authors | Humanities | Social Science | STEM | Other | Average | |------------------------------------|----------|:-------:|:-------:|:-------:|:-------:|:-------:| | [UnifiedQA](https://arxiv.org/abs/2005.00700) | Khashabi et al., 2020 | 45.6 | 56.6 | 40.2 | 54.6 | 48.9 | [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) | Brown et al., 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9 | [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al., 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4 | Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 ### Languages English ## Dataset Structure ### Data Instances An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone?", "choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"], "answer": "D" } ``` ### Data Fields - `question`: a string feature - `choices`: a list of 4 string features - `answer`: a ClassLabel feature ### Data Splits - `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc. - `dev`: 5 examples per subtask, meant for few-shot setting - `test`: there are at least 100 examples per subtask | | auxiliary_train | dev | val | test | | ----- | :------: | :-----: | :-----: | :-----: | | TOTAL | 99842 | 285 | 1531 | 14042 ## Dataset Creation ### Curation Rationale Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn. ### 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 [MIT License](https://github.com/hendrycks/test/blob/master/LICENSE) ### Citation Information If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from: ``` @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} } @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ``` ### Contributions Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
lighteval/mmlu
[ "task_categories:question-answering", "task_ids:multiple-choice-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:mit", "arxiv:2009.03300", "arxiv:2005.00700", "arxiv:2005.14165", "arxiv:2008.02275", "region:us" ]
2023-05-16T08:39:28+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa"], "paperswithcode_id": "mmlu", "pretty_name": "Measuring Massive Multitask Language Understanding", "language_bcp47": ["en-US"], "dataset_info": [{"config_name": "abstract_algebra", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 19328, "num_examples": 100}, {"name": "validation", "num_bytes": 2024, "num_examples": 11}, {"name": "dev", "num_bytes": 830, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160623559}, {"config_name": "anatomy", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 33121, "num_examples": 135}, {"name": "validation", "num_bytes": 3140, "num_examples": 14}, {"name": "dev", "num_bytes": 967, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160638605}, {"config_name": "astronomy", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 46771, "num_examples": 152}, {"name": "validation", "num_bytes": 5027, "num_examples": 16}, {"name": "dev", "num_bytes": 2076, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160655251}, {"config_name": "business_ethics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 33252, "num_examples": 100}, {"name": "validation", "num_bytes": 3038, "num_examples": 11}, {"name": "dev", "num_bytes": 2190, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160639857}, {"config_name": "clinical_knowledge", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 62754, "num_examples": 265}, {"name": "validation", "num_bytes": 6664, "num_examples": 29}, {"name": "dev", "num_bytes": 1210, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160672005}, {"config_name": "college_biology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 48797, "num_examples": 144}, {"name": "validation", "num_bytes": 4819, "num_examples": 16}, {"name": "dev", "num_bytes": 1532, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160656525}, {"config_name": "college_chemistry", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 24708, "num_examples": 100}, {"name": "validation", "num_bytes": 2328, "num_examples": 8}, {"name": "dev", "num_bytes": 1331, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160629744}, {"config_name": "college_computer_science", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 42641, "num_examples": 100}, {"name": "validation", "num_bytes": 4663, "num_examples": 11}, {"name": "dev", "num_bytes": 2765, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160651446}, {"config_name": "college_mathematics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 24711, "num_examples": 100}, {"name": "validation", "num_bytes": 2668, "num_examples": 11}, {"name": "dev", "num_bytes": 1493, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160630249}, {"config_name": "college_medicine", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 82397, "num_examples": 173}, {"name": "validation", "num_bytes": 7909, "num_examples": 22}, {"name": "dev", "num_bytes": 1670, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160693353}, {"config_name": "college_physics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 30181, "num_examples": 102}, {"name": "validation", "num_bytes": 3490, "num_examples": 11}, {"name": "dev", "num_bytes": 1412, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160636460}, {"config_name": "computer_security", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 27124, "num_examples": 100}, {"name": "validation", "num_bytes": 4549, "num_examples": 11}, {"name": "dev", "num_bytes": 1101, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160634151}, {"config_name": "conceptual_physics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 40709, "num_examples": 235}, {"name": "validation", "num_bytes": 4474, "num_examples": 26}, {"name": "dev", "num_bytes": 934, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160647494}, {"config_name": "econometrics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 46547, "num_examples": 114}, {"name": "validation", "num_bytes": 4967, "num_examples": 12}, {"name": "dev", "num_bytes": 1644, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160654535}, {"config_name": "electrical_engineering", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 25142, "num_examples": 145}, {"name": "validation", "num_bytes": 2903, "num_examples": 16}, {"name": "dev", "num_bytes": 972, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160630394}, {"config_name": "elementary_mathematics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 70108, "num_examples": 378}, {"name": "validation", "num_bytes": 8988, "num_examples": 41}, {"name": "dev", "num_bytes": 1440, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160681913}, {"config_name": "formal_logic", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 49785, "num_examples": 126}, {"name": "validation", "num_bytes": 6252, "num_examples": 14}, {"name": "dev", "num_bytes": 1757, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160659171}, {"config_name": "global_facts", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 18403, "num_examples": 100}, {"name": "validation", "num_bytes": 1865, "num_examples": 10}, {"name": "dev", "num_bytes": 1229, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160622874}, {"config_name": "high_school_biology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 109732, "num_examples": 310}, {"name": "validation", "num_bytes": 11022, "num_examples": 32}, {"name": "dev", "num_bytes": 1673, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160723804}, {"config_name": "high_school_chemistry", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 58464, "num_examples": 203}, {"name": "validation", "num_bytes": 7092, "num_examples": 22}, {"name": "dev", "num_bytes": 1220, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160668153}, {"config_name": "high_school_computer_science", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 44476, "num_examples": 100}, {"name": "validation", "num_bytes": 3343, "num_examples": 9}, {"name": "dev", "num_bytes": 2918, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160652114}, {"config_name": "high_school_european_history", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 270300, "num_examples": 165}, {"name": "validation", "num_bytes": 29632, "num_examples": 18}, {"name": "dev", "num_bytes": 11564, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160912873}, {"config_name": "high_school_geography", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 42034, "num_examples": 198}, {"name": "validation", "num_bytes": 4332, "num_examples": 22}, {"name": "dev", "num_bytes": 1403, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160649146}, {"config_name": "high_school_government_and_politics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 66074, "num_examples": 193}, {"name": "validation", "num_bytes": 7063, "num_examples": 21}, {"name": "dev", "num_bytes": 1779, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160676293}, {"config_name": "high_school_macroeconomics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 117687, "num_examples": 390}, {"name": "validation", "num_bytes": 13020, "num_examples": 43}, {"name": "dev", "num_bytes": 1328, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160733412}, {"config_name": "high_school_mathematics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 54854, "num_examples": 270}, {"name": "validation", "num_bytes": 5765, "num_examples": 29}, {"name": "dev", "num_bytes": 1297, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160663293}, {"config_name": "high_school_microeconomics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 75703, "num_examples": 238}, {"name": "validation", "num_bytes": 7553, "num_examples": 26}, {"name": "dev", "num_bytes": 1298, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160685931}, {"config_name": "high_school_physics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 59538, "num_examples": 151}, {"name": "validation", "num_bytes": 6771, "num_examples": 17}, {"name": "dev", "num_bytes": 1489, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160669175}, {"config_name": "high_school_psychology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 159407, "num_examples": 545}, {"name": "validation", "num_bytes": 17269, "num_examples": 60}, {"name": "dev", "num_bytes": 1905, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160779958}, {"config_name": "high_school_statistics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 110702, "num_examples": 216}, {"name": "validation", "num_bytes": 9997, "num_examples": 23}, {"name": "dev", "num_bytes": 2528, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160724604}, {"config_name": "high_school_us_history", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 296734, "num_examples": 204}, {"name": "validation", "num_bytes": 31706, "num_examples": 22}, {"name": "dev", "num_bytes": 8864, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160938681}, {"config_name": "high_school_world_history", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 378617, "num_examples": 237}, {"name": "validation", "num_bytes": 45501, "num_examples": 26}, {"name": "dev", "num_bytes": 4882, "num_examples": 5}], "download_size": 166184960, "dataset_size": 161030377}, {"config_name": "human_aging", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 46098, "num_examples": 223}, {"name": "validation", "num_bytes": 4707, "num_examples": 23}, {"name": "dev", "num_bytes": 1008, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160653190}, {"config_name": "human_sexuality", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 32110, "num_examples": 131}, {"name": "validation", "num_bytes": 2421, "num_examples": 12}, {"name": "dev", "num_bytes": 1077, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160636985}, {"config_name": "international_law", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 53531, "num_examples": 121}, {"name": "validation", "num_bytes": 6473, "num_examples": 13}, {"name": "dev", "num_bytes": 2418, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160663799}, {"config_name": "jurisprudence", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 33986, "num_examples": 108}, {"name": "validation", "num_bytes": 3729, "num_examples": 11}, {"name": "dev", "num_bytes": 1303, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160640395}, {"config_name": "logical_fallacies", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 50117, "num_examples": 163}, {"name": "validation", "num_bytes": 5103, "num_examples": 18}, {"name": "dev", "num_bytes": 1573, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160658170}, {"config_name": "machine_learning", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 33880, "num_examples": 112}, {"name": "validation", "num_bytes": 3232, "num_examples": 11}, {"name": "dev", "num_bytes": 2323, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160640812}, {"config_name": "management", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 20002, "num_examples": 103}, {"name": "validation", "num_bytes": 1820, "num_examples": 11}, {"name": "dev", "num_bytes": 898, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160624097}, {"config_name": "marketing", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 63025, "num_examples": 234}, {"name": "validation", "num_bytes": 7394, "num_examples": 25}, {"name": "dev", "num_bytes": 1481, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160673277}, {"config_name": "medical_genetics", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 20864, "num_examples": 100}, {"name": "validation", "num_bytes": 3005, "num_examples": 11}, {"name": "dev", "num_bytes": 1089, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160626335}, {"config_name": "miscellaneous", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 147704, "num_examples": 783}, {"name": "validation", "num_bytes": 14330, "num_examples": 86}, {"name": "dev", "num_bytes": 699, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160764110}, {"config_name": "moral_disputes", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 107818, "num_examples": 346}, {"name": "validation", "num_bytes": 12420, "num_examples": 38}, {"name": "dev", "num_bytes": 1755, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160723370}, {"config_name": "moral_scenarios", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 374026, "num_examples": 895}, {"name": "validation", "num_bytes": 42338, "num_examples": 100}, {"name": "dev", "num_bytes": 2058, "num_examples": 5}], "download_size": 166184960, "dataset_size": 161019799}, {"config_name": "nutrition", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 92410, "num_examples": 306}, {"name": "validation", "num_bytes": 8436, "num_examples": 33}, {"name": "dev", "num_bytes": 2085, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160704308}, {"config_name": "philosophy", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 80073, "num_examples": 311}, {"name": "validation", "num_bytes": 9184, "num_examples": 34}, {"name": "dev", "num_bytes": 988, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160691622}, {"config_name": "prehistory", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 89594, "num_examples": 324}, {"name": "validation", "num_bytes": 10285, "num_examples": 35}, {"name": "dev", "num_bytes": 1878, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160703134}, {"config_name": "professional_accounting", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 124550, "num_examples": 282}, {"name": "validation", "num_bytes": 14372, "num_examples": 31}, {"name": "dev", "num_bytes": 2148, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160742447}, {"config_name": "professional_law", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 1891762, "num_examples": 1534}, {"name": "validation", "num_bytes": 203519, "num_examples": 170}, {"name": "dev", "num_bytes": 6610, "num_examples": 5}], "download_size": 166184960, "dataset_size": 162703268}, {"config_name": "professional_medicine", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 217561, "num_examples": 272}, {"name": "validation", "num_bytes": 23847, "num_examples": 31}, {"name": "dev", "num_bytes": 3807, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160846592}, {"config_name": "professional_psychology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 225899, "num_examples": 612}, {"name": "validation", "num_bytes": 29101, "num_examples": 69}, {"name": "dev", "num_bytes": 2267, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160858644}, {"config_name": "public_relations", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 28760, "num_examples": 110}, {"name": "validation", "num_bytes": 4566, "num_examples": 12}, {"name": "dev", "num_bytes": 1496, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160636199}, {"config_name": "security_studies", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 204844, "num_examples": 245}, {"name": "validation", "num_bytes": 22637, "num_examples": 27}, {"name": "dev", "num_bytes": 5335, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160834193}, {"config_name": "sociology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 66243, "num_examples": 201}, {"name": "validation", "num_bytes": 7184, "num_examples": 22}, {"name": "dev", "num_bytes": 1613, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160676417}, {"config_name": "us_foreign_policy", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 28443, "num_examples": 100}, {"name": "validation", "num_bytes": 3264, "num_examples": 11}, {"name": "dev", "num_bytes": 1611, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160634695}, {"config_name": "virology", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 38759, "num_examples": 166}, {"name": "validation", "num_bytes": 5463, "num_examples": 18}, {"name": "dev", "num_bytes": 1096, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160646695}, {"config_name": "world_religions", "features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": {"class_label": {"names": {"0": "A", "1": "B", "2": "C", "3": "D"}}}}], "splits": [{"name": "auxiliary_train", "num_bytes": 160601377, "num_examples": 99842}, {"name": "test", "num_bytes": 25274, "num_examples": 171}, {"name": "validation", "num_bytes": 2765, "num_examples": 19}, {"name": "dev", "num_bytes": 670, "num_examples": 5}], "download_size": 166184960, "dataset_size": 160630086}]}
2023-06-09T15:36:19+00:00
75829dbc5b0ed1f03b976db7d36ab97c4de92015
# Dataset Card for "gaps_it" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bjoernp/gaps_it
[ "region:us" ]
2023-05-16T08:51:21+00:00
{"dataset_info": {"features": [{"name": "sentences", "dtype": "string"}, {"name": "sentences_it", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 58148054181, "num_examples": 231591358}], "download_size": 34153098691, "dataset_size": 58148054181}}
2023-05-16T09:18:58+00:00
3def28953f6d8d65bde7b6b3956fe36c9791a4de
# LHM-Dienstleistungen-corpus- german public domain texts Datasets created based on data from Munich city administration. ## Data basis Texts taken from the “Dienstleistungsfinder“ of the city of Munich administration. There information about services offered by city is presented online. Information ranges from applying for an ID card to dispose of garbage. - https://stadt.muenchen.de/service/ (Date 11/2022)
it-at-m/LHM-Dienstleistungen-Corpus
[ "task_categories:feature-extraction", "task_categories:text-generation", "size_categories:n<1K", "language:de", "license:mit", "Stadt München", "Bürgerbüro", "Behördendeutsch", "Corpus", "region:us" ]
2023-05-16T08:58:35+00:00
{"language": ["de"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["feature-extraction", "text-generation"], "pretty_name": "LHM Dienstleistungen: Corpus", "tags": ["Stadt M\u00fcnchen", "B\u00fcrgerb\u00fcro", "Beh\u00f6rdendeutsch", "Corpus"], "viewer": false}
2024-01-23T12:28:09+00:00