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
|
|---|---|---|---|---|---|---|
5f22c8d924620cb0aed0dbb6fcd488b98c1b79e6 | # Dataset Card for "shell_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/shell_paths | [
"region:us"
] | 2022-11-10T12:04:16+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 99354502, "num_examples": 3657232}], "download_size": 82635721, "dataset_size": 99354502}} | 2022-11-10T12:04:28+00:00 |
9fd38e27d47abd2e31ea9449d0a3244ef9cdb9e5 | # Dataset Card for "cmake_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/cmake_paths | [
"region:us"
] | 2022-11-10T12:05:46+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14898478, "num_examples": 559316}], "download_size": 7920865, "dataset_size": 14898478}} | 2022-11-10T12:05:55+00:00 |
8d7956373a46b61d5dbbc93eaafac34dbec7f442 | # Dataset Card for "cpp_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/cpp_paths | [
"region:us"
] | 2022-11-10T12:11:26+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 339979633, "num_examples": 13541537}], "download_size": 250743754, "dataset_size": 339979633}} | 2022-11-10T12:11:49+00:00 |
d9fabc34754e7840bbeaae7c93e51ebee7163cf5 | # Dataset Card for "dockerfile_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/dockerfile_paths | [
"region:us"
] | 2022-11-10T12:12:30+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 36265516, "num_examples": 1274173}], "download_size": 23300431, "dataset_size": 36265516}} | 2022-11-10T12:12:39+00:00 |
92274710c3b10948f908f2bcc6ad18d4ae46fcbe |
This dataset simply loads Google's Analysis-Ready Cloud Optimized ERA5 Reanalysis dataset from Google Public Datasets. | openclimatefix/arco-era5 | [
"license:apache-2.0",
"region:us"
] | 2022-11-10T12:14:40+00:00 | {"license": "apache-2.0"} | 2022-11-10T12:15:34+00:00 |
399d15484ac32ea5b97b3a975ff6ba2a1ee921e6 | jjoverv/redescubrastyle | [
"license:openrail",
"region:us"
] | 2022-11-10T12:14:44+00:00 | {"license": "openrail"} | 2022-11-10T12:14:44+00:00 |
|
adda3417bbc9cb098de689b7ff70c50abe247735 | # Dataset Card for "html_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/html_paths | [
"region:us"
] | 2022-11-10T12:27:27+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 904459341, "num_examples": 32312575}], "download_size": 586813218, "dataset_size": 904459341}} | 2022-11-10T12:28:11+00:00 |
4571f733649eb652dc3f5177bef1ec9d50b23f76 | # Dataset Card for "lua_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/lua_paths | [
"region:us"
] | 2022-11-10T12:28:24+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21014952, "num_examples": 808034}], "download_size": 11839424, "dataset_size": 21014952}} | 2022-11-10T12:28:33+00:00 |
c6c5c093ae298bc26353208f8fde21b423857736 | # Dataset Card for "css_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/css_paths | [
"region:us"
] | 2022-11-10T12:31:19+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 158651499, "num_examples": 5726933}], "download_size": 138140586, "dataset_size": 158651499}} | 2022-11-10T12:31:36+00:00 |
c63e568f42042af30725cbb49a850dd5baa5f528 | # Dataset Card for "visual-basic_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/visual-basic_paths | [
"region:us"
] | 2022-11-10T12:31:42+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5643571, "num_examples": 200013}], "download_size": 1586937, "dataset_size": 5643571}} | 2022-11-10T12:31:50+00:00 |
a8591f85e6cd6f947bdd9363baefd5cc922951ad | # Dataset Card for "sql_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/sql_paths | [
"region:us"
] | 2022-11-10T12:32:20+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35050567, "num_examples": 1267490}], "download_size": 23626806, "dataset_size": 35050567}} | 2022-11-10T12:32:29+00:00 |
d0c81062b8b7d00c6beb0ef721f1c81d97ead65d | # Dataset Card for "tex_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/tex_paths | [
"region:us"
] | 2022-11-10T12:32:40+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12350897, "num_examples": 448193}], "download_size": 6578383, "dataset_size": 12350897}} | 2022-11-10T12:32:48+00:00 |
9ff5bde3da778a10f68fc440cebdf798f08e6c61 | # Dataset Card for "php_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/php_paths | [
"region:us"
] | 2022-11-10T12:39:49+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 910857017, "num_examples": 34179448}], "download_size": 787090086, "dataset_size": 910857017}} | 2022-11-10T12:40:45+00:00 |
69dc5d4d868e74ccbb29f887b6fdbeded3447ffd | # Dataset Card for "julia_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/julia_paths | [
"region:us"
] | 2022-11-10T12:40:54+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14862518, "num_examples": 473425}], "download_size": 7932474, "dataset_size": 14862518}} | 2022-11-10T12:41:03+00:00 |
ff60a980c29a5af1c4dddbbdbc475fe6106ad698 | # Dataset Card for "assembly_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/assembly_paths | [
"region:us"
] | 2022-11-10T12:41:09+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7492209, "num_examples": 324343}], "download_size": 2131380, "dataset_size": 7492209}} | 2022-11-10T12:41:17+00:00 |
a4b3aab622234840a05f4c79520adbc9a7179844 | # Dataset Card for "makefile_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/makefile_paths | [
"region:us"
] | 2022-11-10T12:41:35+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28586262, "num_examples": 1087444}], "download_size": 22517681, "dataset_size": 28586262}} | 2022-11-10T12:41:44+00:00 |
933d86766ee35adaa6be89d23a30229113bf7f35 | # Dataset Card for "javascript_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/javascript_paths | [
"region:us"
] | 2022-11-10T12:54:11+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1086652130, "num_examples": 39278951}], "download_size": 931947481, "dataset_size": 1086652130}} | 2022-11-10T12:55:18+00:00 |
2e12d8e250ea5827ad64d8481e2dd01122c0bb91 | # Dataset Card for "markdown_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/markdown_paths | [
"region:us"
] | 2022-11-10T13:01:33+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 821714901, "num_examples": 28965353}], "download_size": 663085249, "dataset_size": 821714901}} | 2022-11-10T13:02:25+00:00 |
b60d3d46f9388d56418c4f7fea1904c3cd6bc4bc | # Dataset Card for "batchfile_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/batchfile_paths | [
"region:us"
] | 2022-11-10T13:02:31+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11616420, "num_examples": 423086}], "download_size": 8986923, "dataset_size": 11616420}} | 2022-11-10T13:02:40+00:00 |
c52da2ae2bd53896544c8ab1ee499f8df97adeb4 | LazyLabChina/shuimodancai | [
"license:afl-3.0",
"region:us"
] | 2022-11-10T13:05:15+00:00 | {"license": "afl-3.0"} | 2022-11-12T01:48:13+00:00 |
|
a480c7b3807ccb6f174055ee918386bc4016975f | # Dataset Card for "c_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/c_paths | [
"region:us"
] | 2022-11-10T13:08:20+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 508253008, "num_examples": 19878729}], "download_size": 359733499, "dataset_size": 508253008}} | 2022-11-10T13:08:59+00:00 |
66d6e22b5b7a0acf146d5c9bf9a89934b9012d07 | # Dataset Card for "ruby_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/ruby_paths | [
"region:us"
] | 2022-11-10T13:10:12+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 169345268, "num_examples": 6390966}], "download_size": 118905787, "dataset_size": 169345268}} | 2022-11-10T13:10:29+00:00 |
aa66f3d11cfbdf7c557af0a7252cb2550413770d | # Dataset Card for "haskell_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/haskell_paths | [
"region:us"
] | 2022-11-10T13:10:45+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23059551, "num_examples": 921236}], "download_size": 12139516, "dataset_size": 23059551}} | 2022-11-10T13:10:54+00:00 |
bc25ad35e65d45db2445c945f47da9b4ed4fcca4 | # Dataset Card for "fortran_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/fortran_paths | [
"region:us"
] | 2022-11-10T13:11:00+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5773596, "num_examples": 243762}], "download_size": 1463437, "dataset_size": 5773596}} | 2022-11-10T13:11:08+00:00 |
5b7e5b14fa32bbb804ec50fab5d5b2277e81d440 | siberspace/gaelle2 | [
"region:us"
] | 2022-11-10T13:12:05+00:00 | {} | 2022-11-10T13:12:33+00:00 |
|
c2e203ac9c1a0484cf21a7fd6fff2104f0031b31 | # Dataset Card for "c-sharp_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/c-sharp_paths | [
"region:us"
] | 2022-11-10T13:15:33+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 586063746, "num_examples": 20539828}], "download_size": 439948378, "dataset_size": 586063746}} | 2022-11-10T13:16:06+00:00 |
9a8a57728e5c5b7dd93f45e9cf93b45d0b8ab54a | # Dataset Card for "rust_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/rust_paths | [
"region:us"
] | 2022-11-10T13:17:08+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 71297350, "num_examples": 3087525}], "download_size": 49706578, "dataset_size": 71297350}} | 2022-11-10T13:17:18+00:00 |
f3f85f8988d13dc427f042efc6e603481d8d3a08 | # Dataset Card for "typescript_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/typescript_paths | [
"region:us"
] | 2022-11-10T13:21:42+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 536493166, "num_examples": 19441648}], "download_size": 434213958, "dataset_size": 536493166}} | 2022-11-10T13:22:14+00:00 |
70b7bb8cd6c36c701f62f41b0635c8124ac8336d | # Dataset Card for "scala_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/scala_paths | [
"region:us"
] | 2022-11-10T13:22:51+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 68488532, "num_examples": 2635793}], "download_size": 35187635, "dataset_size": 68488532}} | 2022-11-10T13:23:00+00:00 |
a149c3a5039b193bc29a0f57dada1982400f30b2 | Triton100/KiryuuMichiru2 | [
"license:bsd",
"region:us"
] | 2022-11-10T13:25:55+00:00 | {"license": "bsd"} | 2022-11-10T16:02:59+00:00 |
|
03199d030fdc050ffa9df8a29028d3128fea03ad | # Dataset Card for "python_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/python_paths | [
"region:us"
] | 2022-11-10T13:28:40+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 636121755, "num_examples": 23578465}], "download_size": 550836738, "dataset_size": 636121755}} | 2022-11-10T13:29:19+00:00 |
471372c3b0255eff62320029c85ab2cd40afd8dc | # Dataset Card for "perl_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/perl_paths | [
"region:us"
] | 2022-11-10T13:29:30+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14604805, "num_examples": 554602}], "download_size": 4964930, "dataset_size": 14604805}} | 2022-11-10T13:29:38+00:00 |
6f7ef421b610ca5c88bbb30883740e3d040127aa | # Dataset Card for "go_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/go_paths | [
"region:us"
] | 2022-11-10T13:32:57+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 301556518, "num_examples": 12078461}], "download_size": 219608192, "dataset_size": 301556518}} | 2022-11-10T13:33:17+00:00 |
275fd28622d04fe9cba55698fc89a51fef7c5a80 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: librispeech-train-100
size_categories: []
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
task_ids: []---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
| bgstud/data-librispeech100 | [
"region:us"
] | 2022-11-10T13:33:32+00:00 | {} | 2022-11-10T13:39:01+00:00 |
3b71db65661fda9f992bae1b64de5422d46b96fd | # Dataset Card for "java_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/java_paths | [
"region:us"
] | 2022-11-10T13:42:58+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1168673674, "num_examples": 43005815}], "download_size": 919178767, "dataset_size": 1168673674}} | 2022-11-10T13:44:12+00:00 |
ccad17bea366c31a400121ceab9c11b60811f7f2 | # Dataset Card for "powershell_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cakiki/powershell_paths | [
"region:us"
] | 2022-11-10T13:44:21+00:00 | {"dataset_info": {"features": [{"name": "repository_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15534114, "num_examples": 521952}], "download_size": 7947926, "dataset_size": 15534114}} | 2022-11-10T13:44:30+00:00 |
9fabb23ada1c8a22dfda37e6e417d72afd5564a6 | boxzero/testing | [
"license:afl-3.0",
"region:us"
] | 2022-11-10T15:21:54+00:00 | {"license": "afl-3.0"} | 2022-11-10T15:21:54+00:00 |
|
dde1418cf31cd68c426c5c6a9da63125f45469bc | kartucha/kartucha | [
"region:us"
] | 2022-11-10T15:24:12+00:00 | {} | 2022-11-10T15:24:41+00:00 |
|
fa67acb659b8b5a3d212c786a1f9ce545d20ee71 | ChristophSchuhmann/imagenet1k-by-SD-V1.4 | [
"license:apache-2.0",
"region:us"
] | 2022-11-10T15:33:02+00:00 | {"license": "apache-2.0"} | 2022-11-10T15:33:02+00:00 |
|
76e45f52830d51bef8c84e6bb4319c5b9c45c012 | Aubing-Hou/KillersofThreeKingdoms | [
"license:openrail",
"region:us"
] | 2022-11-10T15:50:54+00:00 | {"license": "openrail"} | 2022-11-10T15:50:54+00:00 |
|
deef869dedc5fc3fdf55dc905e8b4b27e379eb85 | rexelecaps/Dateset | [
"license:unknown",
"region:us"
] | 2022-11-10T15:57:17+00:00 | {"license": "unknown"} | 2022-11-10T15:59:26+00:00 |
|
be8294568a7c067fc2262334b7d9841b333f080a | bishalbaaniya/my_en | [
"license:artistic-2.0",
"region:us"
] | 2022-11-10T19:36:46+00:00 | {"license": "artistic-2.0"} | 2022-11-11T03:18:22+00:00 |
|
e1e61ab74cb5165978b478962f0432a3209e194f |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
| bgstud/libri | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | 2022-11-10T19:48:45+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["token-classification-other-acronym-identification"], "paperswithcode_id": "acronym-identification", "pretty_name": "Acronym Identification Dataset", "train-eval-index": [{"col_mapping": {"labels": "tags", "tokens": "tokens"}, "config": "default", "splits": {"eval_split": "test"}, "task": "token-classification", "task_id": "entity_extraction"}]} | 2022-11-10T20:03:23+00:00 |
8e566ce5b7c2fd05f61fb2b2fe4f2520706607d1 | nateraw/video-demo | [
"license:mit",
"region:us"
] | 2022-11-10T20:54:09+00:00 | {"license": "mit"} | 2022-11-10T21:16:49+00:00 |
|
30db3effcfc07b638291cfcff248b84dbd8013db | # Dataset Card for "saf_micro_job_german"
## 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)
- [Annotation process](#annotation-process)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022)
### Dataset Summary
Short Answer Feedback (SAF) dataset is a short answer dataset introduced in [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 8 German questions used in micro-job training on the crowd-worker platform appJobber - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the [saf_communication_networks_english](https://huggingface.co/datasets/Short-Answer-Feedback/saf_communication_networks_english) dataset to examine the English subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german).
### Supported Tasks and Leaderboards
- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.
### Languages
The questions, reference answers, provided answers and the answer feedback in the dataset are written in German.
## Dataset Structure
### Data Instances
An example of an entry of the training split looks as follows.
```
{
"id": "1",
"question": "Frage 1: Ist das eine Frage?",
"reference_answer": "Ja, das ist eine Frage.",
"provided_answer": "Ich bin mir sicher, dass das eine Frage ist.",
"answer_feedback": "Korrekt!",
"verification_feedback": "Correct",
"score": 1
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature (UUID4 in HEX format).
- `question`: a `string` feature representing a question.
- `reference_answer`: a `string` feature representing a reference answer to the question.
- `provided_answer`: a `string` feature representing an answer that was provided for a particular question.
- `answer_feedback`: a `string` feature representing the feedback given to the provided answers.
- `verification_feedback`: a `string` feature representing an automatic labeling of the score. It can be `Correct` (`score` = 1), `Incorrect` (`score` = 0) or `Partially correct` (all intermediate scores).
- `score`: a `float64` feature (between 0 and 1) representing the score given to the provided answer.
### Data Splits
The dataset is comprised of four data splits.
- `train`: used for training, contains a set of questions and the provided answers to them.
- `validation`: used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).
- `test_unseen_answers`: used for testing, contains unseen answers to the questions present in the `train` split.
- `test_unseen_questions`: used for testing, contains unseen questions that do not appear in the `train` split.
| Split |train|validation|test_unseen_answers|test_unseen_questions|
|-------------------|----:|---------:|------------------:|--------------------:|
|Number of instances| 1226| 308| 271| 602|
## Dataset Creation
### Annotation Process
Two experienced appJobber employees were selected to evaluate the crowd-worker platform’s answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.
## Additional Information
### Citation Information
```
@inproceedings{filighera-etal-2022-answer,
title = "Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset",
author = "Filighera, Anna and
Parihar, Siddharth and
Steuer, Tim and
Meuser, Tobias and
Ochs, Sebastian",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.587",
doi = "10.18653/v1/2022.acl-long.587",
pages = "8577--8591",
}
```
### Contributions
Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset. | Short-Answer-Feedback/saf_micro_job_german | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"short answer feedback",
"micro job",
"region:us"
] | 2022-11-10T21:21:46+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["de"], "license": "cc-by-4.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "pretty_name": "SAF - Micro Job - German", "tags": ["short answer feedback", "micro job"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "provided_answer", "dtype": "string"}, {"name": "answer_feedback", "dtype": "string"}, {"name": "verification_feedback", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 885526, "num_examples": 1226}, {"name": "validation", "num_bytes": 217946, "num_examples": 308}, {"name": "test_unseen_answers", "num_bytes": 198832, "num_examples": 271}, {"name": "test_unseen_questions", "num_bytes": 545524, "num_examples": 602}], "download_size": 274603, "dataset_size": 1847828}} | 2023-03-31T10:47:23+00:00 |
9358d6f0f87371c0a5f150502b14cb16e382195f | # Dataset Card for "saf_communication_networks_english"
## 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)
- [Annotation process](#annotation-process)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022)
### Dataset Summary
Short Answer Feedback (SAF) dataset is a short answer dataset introduced in [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 31 English questions covering a range of college-level communication networks topics - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the [saf_micro_job_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_micro_job_german) dataset to examine the German subset of the original dataset. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at [saf_legal_domain_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_legal_domain_german).
### Supported Tasks and Leaderboards
- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformers in order to generate automatic short answer feedback.
### Languages
The questions, reference answers, provided answers and the answer feedback in the dataset are written in English.
## Dataset Structure
### Data Instances
An example of an entry of the training split looks as follows.
```
{
"id": "1",
"question": "Is this a question?",
"reference_answer": "Yes, that is a question.",
"provided_answer": "I'm certain this is a question.",
"answer_feedback": "The response is correct.",
"verification_feedback": "Correct",
"score": 1
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature (UUID4 in HEX format).
- `question`: a `string` feature representing a question.
- `reference_answer`: a `string` feature representing a reference answer to the question.
- `provided_answer`: a `string` feature representing an answer that was provided for a particular question.
- `answer_feedback`: a `string` feature representing the feedback given to the provided answers.
- `verification_feedback`: a `string` feature representing an automatic labeling of the score. It can be `Correct` (`score` = maximum points achievable), `Incorrect` (`score` = 0) or `Partially correct` (all intermediate scores).
- `score`: a `float64` feature representing the score given to the provided answer. For most questions it ranges from 0 to 1.
### Data Splits
The dataset is comprised of four data splits.
- `train`: used for training, contains a set of questions and the provided answers to them.
- `validation`: used for validation, contains a set of questions and the provided answers to them (derived from the original training set defined in the paper).
- `test_unseen_answers`: used for testing, contains unseen answers to the questions present in the `train` split.
- `test_unseen_questions`: used for testing, contains unseen questions that do not appear in the `train` split.
| Split |train|validation|test_unseen_answers|test_unseen_questions|
|-------------------|----:|---------:|------------------:|--------------------:|
|Number of instances| 1700| 427| 375| 479|
## Dataset Creation
### Annotation Process
Two graduate students who had completed the communication networks course were selected to evaluate the answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.
## Additional Information
### Citation Information
```
@inproceedings{filighera-etal-2022-answer,
title = "Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset",
author = "Filighera, Anna and
Parihar, Siddharth and
Steuer, Tim and
Meuser, Tobias and
Ochs, Sebastian",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.587",
doi = "10.18653/v1/2022.acl-long.587",
pages = "8577--8591",
}
```
### Contributions
Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset. | Short-Answer-Feedback/saf_communication_networks_english | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"short answer feedback",
"communication networks",
"region:us"
] | 2022-11-10T21:22:13+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["en"], "license": "cc-by-4.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "pretty_name": "SAF - Communication Networks - English", "tags": ["short answer feedback", "communication networks"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "reference_answer", "dtype": "string"}, {"name": "provided_answer", "dtype": "string"}, {"name": "answer_feedback", "dtype": "string"}, {"name": "verification_feedback", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 2363828, "num_examples": 1700}, {"name": "validation", "num_bytes": 592869, "num_examples": 427}, {"name": "test_unseen_answers", "num_bytes": 515669, "num_examples": 375}, {"name": "test_unseen_questions", "num_bytes": 777945, "num_examples": 479}], "download_size": 941169, "dataset_size": 4250311}} | 2023-03-31T10:46:04+00:00 |
8024654c829eb2cc7d6b5d64ebc2fbf8c42faf27 | bishalbaaniya/my_en_2 | [
"license:apache-2.0",
"region:us"
] | 2022-11-10T22:37:16+00:00 | {"license": "apache-2.0"} | 2022-11-10T22:56:24+00:00 |
|
dfc63068215c270ac0f6702228fd80ea2ae170a5 | # TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages
This repo contains the code and data for the EMNLP 2022 findings paper TyDiP: A Dataset for Politeness Classification in Nine Typologically Diverse Languages which can be found [here](https://aclanthology.org/2022.findings-emnlp.420/).
## Data
The TyDiP dataset is licensed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
The `data` folder contains the different files we release as part of the TyDiP dataset. The TyDiP dataset comprises of an English train set and English test set that are adapted from the Stanford Politeness Corpus, and test data in 9 more languages (Hindi, Korean, Spanish, Tamil, French, Vietnamese, Russian, Afrikaans, Hungarian) that we annotated.
```
data/
├── all
├── binary
└── unlabelled_train_sets
```
`data/all` consists of the complete train and test sets.
`data/binary` is a filtered version of the above where sentences from the top and bottom 25 percentile of scores is only present. This is the data that we used for training and evaluation in the paper.
`data/unlabelled_train_sets`
## Code
`politeness_regresor.py` is used for training and evaluation of transformer models
To train a model
```
python politeness_regressor.py --train_file data/binary/en_train_binary.csv --test_file data/binary/en_test_binary.csv --model_save_location model.pt --pretrained_model xlm-roberta-large --gpus 1 --batch_size 4 --accumulate_grad_batches 8 --max_epochs 5 --checkpoint_callback False --logger False --precision 16 --train --test --binary --learning_rate 5e-6
```
To test this trained model on $lang
```
python politeness_regressor.py --test_file data/binary/${lang}_test_binary.csv --load_model model.pt --gpus 1 --batch_size 32 --test --binary
```
## Pretrained Model
XLM-Roberta Large finetuned on the English train set (as discussed and evaluated in the paper) can be found [here](https://huggingface.co/Genius1237/xlm-roberta-large-tydip)
## Politeness Strategies
`strategies` contains the processed strategy lexicon for different languages. `strategies/learnt_strategies.xlsx` contains the human edited strategies for 4 langauges
## Annotation Interface
`annotation.html` contains the UI used for conducting data annotation
## Citation
If you use the English train or test data, please cite the Stanford Politeness Dataset
```
@inproceedings{danescu-niculescu-mizil-etal-2013-computational,
title = "A computational approach to politeness with application to social factors",
author = "Danescu-Niculescu-Mizil, Cristian and
Sudhof, Moritz and
Jurafsky, Dan and
Leskovec, Jure and
Potts, Christopher",
booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P13-1025",
pages = "250--259",
}
```
If you use the test data from the 9 target languages, please cite our paper
```
@inproceedings{srinivasan-choi-2022-tydip,
title = "{T}y{D}i{P}: A Dataset for Politeness Classification in Nine Typologically Diverse Languages",
author = "Srinivasan, Anirudh and
Choi, Eunsol",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.420",
pages = "5723--5738",
}
```
| Genius1237/TyDiP | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"language:en",
"language:hi",
"language:ko",
"language:es",
"language:ta",
"language:fr",
"language:vi",
"language:ru",
"language:af",
"language:hu",
"license:cc-by-4.0",
"politeness",
"wikipedia",
"multilingual",
"region:us"
] | 2022-11-11T01:08:56+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en", "hi", "ko", "es", "ta", "fr", "vi", "ru", "af", "hu"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "TyDiP", "tags": ["politeness", "wikipedia", "multilingual"]} | 2023-10-15T04:14:26+00:00 |
c08c13295ebd8111ab96879ceba43b99ec28afdb | # Dataset Card for "SemanticScholarAbstracts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | KaiserML/SemanticScholarAbstracts | [
"region:us"
] | 2022-11-11T02:45:31+00:00 | {"dataset_info": {"features": [{"name": "corpusid", "dtype": "int64"}, {"name": "openaccessinfo", "struct": [{"name": "externalids", "struct": [{"name": "ACL", "dtype": "string"}, {"name": "ArXiv", "dtype": "string"}, {"name": "DOI", "dtype": "string"}, {"name": "MAG", "dtype": "string"}, {"name": "PubMedCentral", "dtype": "string"}]}, {"name": "license", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "url", "dtype": "string"}]}, {"name": "abstract", "dtype": "string"}, {"name": "updated", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59461773143.463005, "num_examples": 48314588}], "download_size": 37596463269, "dataset_size": 59461773143.463005}} | 2022-11-11T03:47:32+00:00 |
8491055a606b9b1ec690b39e36fbdf1fddb4c4bc | # Dataset Card for "lab"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | claytonsamples/lab | [
"region:us"
] | 2022-11-11T02:53:57+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "kimwipes", "1": "nitrile gloves", "2": "petri dish", "3": "serological pipette"}}}}], "splits": [{"name": "train", "num_bytes": 22915125.09, "num_examples": 1415}], "download_size": 19042401, "dataset_size": 22915125.09}} | 2022-11-11T02:58:25+00:00 |
13462a42e3375e80a9bd46a64c58e1bcaba77874 | # Dataset Card for "rvl_cdip_400_train_val_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Format
````
DatasetDict({
test: Dataset({
features: ['image', 'label', 'ground_truth'],
num_rows: 1600
})
train: Dataset({
features: ['image', 'label', 'ground_truth'],
num_rows: 6400
})
validation: Dataset({
features: ['image', 'label', 'ground_truth'],
num_rows: 1600
})
})
```` | jinhybr/rvl_cdip_400_train_val_test | [
"region:us"
] | 2022-11-11T04:01:53+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "letter", "1": "form", "2": "email", "3": "handwritten", "4": "advertisement", "5": "scientific report", "6": "scientific publication", "7": "specification", "8": "file folder", "9": "news article", "10": "budget", "11": "invoice", "12": "presentation", "13": "questionnaire", "14": "resume", "15": "memo"}}}}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 197669272.0, "num_examples": 1600}, {"name": "train", "num_bytes": 781258280.0, "num_examples": 6400}, {"name": "validation", "num_bytes": 191125740.0, "num_examples": 1600}], "download_size": 1101475597, "dataset_size": 1170053292.0}} | 2022-11-11T15:58:02+00:00 |
091aec1e2384a20b2b36eb96177755ca13dd0b42 | The small 20K version of the Pubmed-RCT dataset by Dernoncourt et al (2017).
```
@article{dernoncourt2017pubmed,
title={Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts},
author={Dernoncourt, Franck and Lee, Ji Young},
journal={arXiv preprint arXiv:1710.06071},
year={2017}
}
```
Note: This is the cleaned up version by Jin and Szolovits (2018).
```
@article{jin2018hierarchical,
title={Hierarchical neural networks for sequential sentence classification in medical scientific abstracts},
author={Jin, Di and Szolovits, Peter},
journal={arXiv preprint arXiv:1808.06161},
year={2018}
}
``` | armanc/pubmed-rct20k | [
"region:us"
] | 2022-11-11T04:20:56+00:00 | {} | 2022-11-11T08:23:24+00:00 |
d9327e0fa300d66c0c577330a624a39626f1192e | This is the ScientificQA dataset by Saikh et al (2022).
```
@article{10.1007/s00799-022-00329-y,
author = {Saikh, Tanik and Ghosal, Tirthankar and Mittal, Amish and Ekbal, Asif and Bhattacharyya, Pushpak},
title = {ScienceQA: A Novel Resource for Question Answering on Scholarly Articles},
year = {2022},
journal = {Int. J. Digit. Libr.},
month = {sep}
}
| armanc/ScienceQA | [
"region:us"
] | 2022-11-11T05:03:56+00:00 | {} | 2022-11-11T08:34:35+00:00 |
6c6b552186533303a3f2153e6cd2b931ba8e2434 |
# Dataset Card for IDK-MRC
## Dataset Description
- **Repository:** [rifkiaputri/IDK-MRC](https://github.com/rifkiaputri/IDK-MRC)
- **Paper:** [PDF](https://aclanthology.org/2022.emnlp-main.465/)
- **Point of Contact:** [rifkiaputri](https://github.com/rifkiaputri)
### Dataset Summary
I(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA, the new unanswerable question in IDK-MRC is generated using a question generation model and human-written question. Each paragraph in the dataset has a set of answerable and unanswerable questions with the corresponding answer.
### Supported Tasks
IDK-MRC is mainly intended to train Machine Reading Comprehension or extractive QA models.
### Languages
Indonesian
## Dataset Structure
### Data Instances
```
{
"context": "Para ilmuwan menduga bahwa megalodon terlihat seperti hiu putih yang lebih kekar, walaupun hiu ini juga mungkin tampak seperti hiu raksasa (Cetorhinus maximus) atau hiu harimau-pasir (Carcharias taurus). Hewan ini dianggap sebagai salah satu predator terbesar dan terkuat yang pernah ada, dan fosil-fosilnya sendiri menunjukkan bahwa panjang maksimal hiu raksasa ini mencapai 18 m, sementara rata-rata panjangnya berkisar pada angka 10,5 m. Rahangnya yang besar memiliki kekuatan gigitan antara 110.000 hingga 180.000 newton. Gigi mereka tebal dan kuat, dan telah berevolusi untuk menangkap mangsa dan meremukkan tulang.",
"qas":
[
{
"id": "indonesian--6040202845759439489-1",
"is_impossible": false,
"question": "Apakah jenis hiu terbesar di dunia ?",
"answers":
[
{
"text": "megalodon",
"answer_start": 27
}
]
},
{
"id": "indonesian-0426116372962619813-unans-h-2",
"is_impossible": true,
"question": "Apakah jenis hiu terkecil di dunia?",
"answers":
[]
},
{
"id": "indonesian-2493757035872656854-unans-h-2",
"is_impossible": true,
"question": "Apakah jenis hiu betina terbesar di dunia?",
"answers":
[]
}
]
}
```
### Data Fields
Each instance has several fields:
- `context`: context passage/paragraph as a string
- `qas`: list of questions related to the `context`
- `id`: question ID as a string
- `is_impossible`: whether the question is unanswerable (impossible to answer) or not as a boolean
- `question`: question as a string
- `answers`: list of answers
- `text`: answer as a string
- `answer_start`: answer start index as an integer
### Data Splits
- `train`: 9,332 (5,042 answerable, 4,290 unanswerable)
- `valid`: 764 (382 answerable, 382 unanswerable)
- `test`: 844 (422 answerable, 422 unanswerable)
## Dataset Creation
### Curation Rationale
IDK-MRC dataset is built based on the existing paragraph and answerable questions (ans) in TyDiQA-GoldP (Clark et al., 2020). The new unanswerable questions are automatically generated using the combination of mT5 (Xue et al., 2021) and XLM-R (Conneau et al., 2020) models, which are then manually verified by human annotators (filtered ans and filtered unans). We also asked the annotators to manually write additional unanswerable questions as described in §3.3 (additional unans). Each paragraphs in the final dataset will have a set of filtered ans, filtered unans, and additional unans questions.
### Annotations
#### Annotation process
In our dataset collection pipeline, the annotators are asked to validate the model-generated unanswerable questions and write a new additional unanswerable questions.
#### Who are the annotators?
We recruit four annotators with 2+ years of experience in Indonesian NLP annotation using direct recruitment. All of them are Indonesian native speakers who reside in Indonesia (Java Island) and fall under the 18–34 age category. We set the payment to around $7.5 per hour. Given the annotators’ demographic, we ensure that the payment is above the minimum wage rate (as of December 2021). All annotators also have signed the consent form and agreed to participate in this project.
## Considerations for Using the Data
The paragraphs and answerable questions that we utilized to build IDK-MRC dataset are taken from Indonesian subset of TyDiQA-GoldP dataset (Clark et al., 2020), which originates from Wikipedia articles. Since those articles are written from a neutral point of view, the risk of harmful content is minimal. Also, all model-generated questions in our dataset have been validated by human annotators to eliminate the risk of harmful questions. During the manual question generation process, the annotators are also encouraged to avoid producing possibly offensive questions.
Even so, we argue that further assessment is needed before using our dataset and models in real-world applications. This measurement is especially required for the pre-trained language models used in our experiments, namely mT5 (Xue et al., 2021), IndoBERT (Wilie et al., 2020), mBERT (Devlin et al., 2019), and XLM-R (Conneau et al., 2020). These language models are mostly pre-trained on the common-crawl dataset, which may contain harmful biases or stereotypes.
## Additional Information
### Licensing Information
CC BY-SA 4.0
### Citation Information
```bibtex
@inproceedings{putri-oh-2022-idk,
title = "{IDK}-{MRC}: Unanswerable Questions for {I}ndonesian Machine Reading Comprehension",
author = "Putri, Rifki Afina and
Oh, Alice",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.465",
pages = "6918--6933",
}
```
| rifkiaputri/idk-mrc | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|tydiqa",
"language:id",
"license:cc-by-4.0",
"region:us"
] | 2022-11-11T05:56:43+00:00 | {"annotations_creators": ["machine-generated", "expert-generated"], "language_creators": ["machine-generated", "expert-generated"], "language": ["id"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["extended|tydiqa"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "IDK-MRC", "tags": []} | 2023-05-23T06:43:23+00:00 |
22d6519d033e3b433daadb49b7fd258dc8c9d3e3 | # Dataset Card for "bayc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sdotmac/bayc | [
"region:us"
] | 2022-11-11T06:08:14+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 381887922.0, "num_examples": 10000}], "download_size": 378097332, "dataset_size": 381887922.0}} | 2022-11-12T05:19:59+00:00 |
94c29b56186e07b267d8ae2610e94e7c8642048d |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
| bgstud/libri-whisper-raw | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | 2022-11-11T10:03:50+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["token-classification-other-acronym-identification"], "paperswithcode_id": "acronym-identification", "pretty_name": "Acronym Identification Dataset", "train-eval-index": [{"col_mapping": {"labels": "tags", "tokens": "tokens"}, "config": "default", "splits": {"eval_split": "test"}, "task": "token-classification", "task_id": "entity_extraction"}]} | 2022-11-11T10:12:24+00:00 |
f807452b98a80d2daf6b7e84f4a8a55bec9b0d16 |
# Dataset Card for "lmqg/qag_tweetqa"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the [tweet_qa](https://huggingface.co/datasets/tweet_qa). The test set of the original data is not publicly released, so we randomly sampled test questions from the training set.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
English (en)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"paragraph": "I would hope that Phylicia Rashad would apologize now that @missjillscott has! You cannot discount 30 victims who come with similar stories.— JDWhitner (@JDWhitner) July 7, 2015",
"questions": [ "what should phylicia rashad do now?", "how many victims have come forward?" ],
"answers": [ "apologize", "30" ],
"questions_answers": "Q: what should phylicia rashad do now?, A: apologize Q: how many victims have come forward?, A: 30"
}
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|4536 | 583| 583|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
``` | lmqg/qag_tweetqa | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:1k<n<10K",
"source_datasets:tweet_qa",
"language:en",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2022-11-11T11:11:25+00:00 | {"language": "en", "license": "cc-by-sa-4.0", "multilinguality": "monolingual", "size_categories": "1k<n<10K", "source_datasets": "tweet_qa", "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "TweetQA for question generation", "tags": ["question-generation"]} | 2022-12-02T19:16:46+00:00 |
bc4d33ef6c32be2053f5ff60a7768bd82f4b66e5 | Trullser/Datasetfortrain | [
"license:other",
"region:us"
] | 2022-11-11T13:25:08+00:00 | {"license": "other"} | 2022-11-11T13:25:08+00:00 |
|
8742dcd8ff947a290cd4ae6fab8384efd3b414c1 | MartinMu/StandardDifusion | [
"license:openrail",
"region:us"
] | 2022-11-11T14:10:08+00:00 | {"license": "openrail"} | 2022-11-11T16:02:28+00:00 |
|
8cf5f918e10ac5cc98e8ba9bb962e6994c19eb43 | Yubing/standardBB | [
"license:openrail",
"region:us"
] | 2022-11-11T14:10:15+00:00 | {"license": "openrail"} | 2022-11-11T14:10:16+00:00 |
|
4ef68edef4dfb32234961dc0a95f07af7562baaa | assq/standard1 | [
"license:openrail",
"region:us"
] | 2022-11-11T14:10:20+00:00 | {"license": "openrail"} | 2022-11-11T14:11:23+00:00 |
|
421ab444b7496ceadc2d18876d53e471131cbca0 | aemska/stuhl | [
"license:openrail",
"region:us"
] | 2022-11-11T14:10:20+00:00 | {"license": "openrail"} | 2022-11-11T14:12:36+00:00 |
|
d1f2a4184134247fa0fbd5db8d7324ef8792c6f8 |
# Dataset Card for "lmqg/qag_squad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the SQuAD.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
English (en)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"paragraph": "\"4 Minutes\" was released as the album's lead single and peaked at number three on the Billboard Hot 100. It was Madonna's 37th top-ten hit on the chart—it pushed Madonna past Elvis Presley as the artist with the most top-ten hits. In the UK she retained her record for the most number-one singles for a female artist; \"4 Minutes\" becoming her thirteenth. At the 23rd Japan Gold Disc Awards, Madonna received her fifth Artist of the Year trophy from Recording Industry Association of Japan, the most for any artist. To further promote the album, Madonna embarked on the Sticky & Sweet Tour; her first major venture with Live Nation. With a gross of $280 million, it became the highest-grossing tour by a solo artist then, surpassing the previous record Madonna set with the Confessions Tour; it was later surpassed by Roger Waters' The Wall Live. It was extended to the next year, adding new European dates, and after it ended, the total gross was $408 million.",
"questions": [
"Which single was released as the album's lead single?",
"Madonna surpassed which artist with the most top-ten hits?",
"4 minutes became Madonna's which number one single in the UK?",
"What is the name of the first tour with Live Nation?",
"How much did Stick and Sweet Tour grossed?"
],
"answers": [
"4 Minutes",
"Elvis Presley",
"thirteenth",
"Sticky & Sweet Tour",
"$280 million,"
],
"questions_answers": "question: Which single was released as the album's lead single?, answer: 4 Minutes | question: Madonna surpassed which artist with the most top-ten hits?, answer: Elvis Presley | question: 4 minutes became Madonna's which number one single in the UK?, answer: thirteenth | question: What is the name of the first tour with Live Nation?, answer: Sticky & Sweet Tour | question: How much did Stick and Sweet Tour grossed?, answer: $280 million,"
}
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|16462| 2067 | 2429|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
``` | lmqg/qag_squad | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:1k<n<10K",
"source_datasets:lmqg/qg_squad",
"language:en",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] | 2022-11-11T14:12:30+00:00 | {"language": "en", "license": "cc-by-sa-4.0", "multilinguality": "monolingual", "size_categories": "1k<n<10K", "source_datasets": "lmqg/qg_squad", "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "SQuAD for question generation", "tags": ["question-generation"]} | 2022-12-18T07:39:03+00:00 |
34e0afd3940437fcc80958ac2fd80e72f964e7b4 | Anas-Uddin/Anas-Uddin | [
"license:openrail",
"region:us"
] | 2022-11-11T14:15:30+00:00 | {"license": "openrail"} | 2022-11-11T14:18:43+00:00 |
|
6d62363678647455e3cea5039c1d8a6593e2bbf6 | fdsghfdh/bjbhk | [
"license:openrail",
"region:us"
] | 2022-11-11T14:19:31+00:00 | {"license": "openrail"} | 2022-11-11T14:19:31+00:00 |
|
628487d37579276a90afaac4854807b1ed1a1252 | Yubing/dogs | [
"license:openrail",
"region:us"
] | 2022-11-11T14:53:54+00:00 | {"license": "openrail"} | 2022-11-11T14:54:31+00:00 |
|
63a4bc8a5d87894a886edabbc67257b4ad7213d8 | NiuDaVinci/niu-davinci | [
"license:afl-3.0",
"region:us"
] | 2022-11-11T16:49:21+00:00 | {"license": "afl-3.0"} | 2022-11-11T17:01:29+00:00 |
|
a50653db5bd9fcf01aa163087e2974ba1388f8da | # Dataset Card for "lolita-dress-CHIN"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zhangxinran/lolita-dress-CHIN | [
"region:us"
] | 2022-11-11T17:36:11+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 704987635.0, "num_examples": 993}], "download_size": 701091143, "dataset_size": 704987635.0}} | 2022-11-11T22:34:20+00:00 |
b4c532908d2439912f9d6d9e0d9d14f8cad898f9 | # Dataset Card for "FewShotSGD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/FewShotSGD | [
"region:us"
] | 2022-11-11T19:11:43+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 7583282, "num_examples": 15537}, {"name": "train", "num_bytes": 46458280, "num_examples": 83391}, {"name": "validation", "num_bytes": 6337305, "num_examples": 11960}], "download_size": 6517762, "dataset_size": 60378867}} | 2022-11-11T19:12:01+00:00 |
dfe2f4e628f236b5c7d59042d4fd584dfb84032b | ozanba/final_project_dataset | [
"license:other",
"region:us"
] | 2022-11-11T19:22:14+00:00 | {"license": "other"} | 2022-12-19T20:26:10+00:00 |
|
ac8a3794d6fb430352b38c459b1d77f49f154b60 | # Dataset Card for "olm-wikipedia-20221101-kl-language"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Tristan/olm-wikipedia-20221101-kl-language | [
"region:us"
] | 2022-11-11T19:32:29+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 311164, "num_examples": 297}], "download_size": 191198, "dataset_size": 311164}} | 2022-11-11T19:32:33+00:00 |
2e0a48fb4a7a928800358270f9601420004a0b95 | mozel/Teste | [
"license:openrail",
"region:us"
] | 2022-11-11T19:59:43+00:00 | {"license": "openrail"} | 2022-11-11T19:59:43+00:00 |
|
c755ed348bdb9c918a0ea6a316d9e0f92ec60de6 | # AutoTrain Dataset for project: tweet-es-sent
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tweet-es-sent.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"target": 1,
"text": "1sola vuelta! arauz presidente! 1sola vuelta! todo 1 1sola la 1 es ecdor! por ti!1 por 1 los tuyos!1 por nosotros juntos1 mas de 45 d apoyo popular el 7 se vota 1por la vida por el futuro,por la esperanza guayaquil ec dor es 1"
},
{
"target": 1,
"text": "excelente decisi\u00f3n , las mujeres son importantes y por esa raz\u00f3n, a productos de primera necesidad hay que quitarles el iva "
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "ClassLabel(num_classes=3, names=['0', '1', '2'], id=None)",
"text": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 12400 |
| valid | 3685 |
| erickdp/autotrain-data-tweet-es-sent | [
"task_categories:text-classification",
"region:us"
] | 2022-11-11T21:02:59+00:00 | {"task_categories": ["text-classification"]} | 2022-11-14T09:01:25+00:00 |
427f6aed010aaa987762d262ce4204444343da4e | # Dataset Card for "SGD_Restaurants"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Restaurants | [
"region:us"
] | 2022-11-11T21:15:54+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2860131.272837388, "num_examples": 9906}, {"name": "test", "num_bytes": 163, "num_examples": 1}], "download_size": 1155851, "dataset_size": 2860294.272837388}} | 2023-03-21T20:51:19+00:00 |
32825c9d2ebb9e0e1955f2692361c2544f09f407 | # Dataset Card for "SGD_Media"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Media | [
"region:us"
] | 2022-11-11T21:16:07+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1404611.2159709618, "num_examples": 6060}, {"name": "test", "num_bytes": 330, "num_examples": 1}], "download_size": 529801, "dataset_size": 1404941.2159709618}} | 2023-03-21T20:51:44+00:00 |
6b1bea67758e47cf0ffedd25a97d0004941decca | # Dataset Card for "SGD_Events"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Events | [
"region:us"
] | 2022-11-11T21:16:20+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4745618.287889816, "num_examples": 17860}, {"name": "test", "num_bytes": 248, "num_examples": 1}], "download_size": 1966143, "dataset_size": 4745866.287889816}} | 2023-03-21T20:52:11+00:00 |
6e99d903784045fa38d781db7d3151ecc5cef621 | # Dataset Card for "SGD_Music"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Music | [
"region:us"
] | 2022-11-11T21:16:34+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1755419.6172071476, "num_examples": 7554}, {"name": "test", "num_bytes": 193, "num_examples": 1}], "download_size": 694555, "dataset_size": 1755612.6172071476}} | 2023-03-21T20:52:36+00:00 |
ca113ae8c6fee3244146c4018aff3a5f473e6a3f | # Dataset Card for "SGD_Movies"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Movies | [
"region:us"
] | 2022-11-11T21:16:47+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1808099.5360110803, "num_examples": 7219}, {"name": "test", "num_bytes": 297, "num_examples": 1}], "download_size": 729887, "dataset_size": 1808396.5360110803}} | 2023-03-21T20:53:02+00:00 |
971d8e55d9bf7b49bc26bf2c1d63c55d5fa391ef | # Dataset Card for "SGD_Flights"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Flights | [
"region:us"
] | 2022-11-11T21:17:00+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 6377556.63733501, "num_examples": 20682}, {"name": "test", "num_bytes": 282, "num_examples": 1}], "download_size": 2501341, "dataset_size": 6377838.63733501}} | 2023-03-21T20:53:30+00:00 |
9782d4289e2422909272c3942c0a53e4fa0fe3a9 | dlwh/Multi_Legal_Pile | [
"license:cc-by-4.0",
"region:us"
] | 2022-11-11T21:17:05+00:00 | {"license": "cc-by-4.0"} | 2022-11-11T21:17:05+00:00 |
|
c91b9effc8db0cbd56283ff7a4d3b6fc82c2ffd0 | # Dataset Card for "SGD_RideSharing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_RideSharing | [
"region:us"
] | 2022-11-11T21:17:14+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 658561.1466613673, "num_examples": 2515}, {"name": "test", "num_bytes": 188, "num_examples": 1}], "download_size": 242358, "dataset_size": 658749.1466613673}} | 2023-03-21T20:53:54+00:00 |
7e433272415d1cb8830b616792fe04adf63c1c40 | # Dataset Card for "SGD_RentalCars"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_RentalCars | [
"region:us"
] | 2022-11-11T21:17:26+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1685534.5292607802, "num_examples": 5843}, {"name": "test", "num_bytes": 239, "num_examples": 1}], "download_size": 637179, "dataset_size": 1685773.5292607802}} | 2023-03-21T20:54:19+00:00 |
8cc33c27527cded7cc876ed06bc79e3d05196393 | # Dataset Card for "SGD_Buses"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Buses | [
"region:us"
] | 2022-11-11T21:17:39+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2009266.9424069906, "num_examples": 7552}, {"name": "test", "num_bytes": 356, "num_examples": 1}], "download_size": 769749, "dataset_size": 2009622.9424069906}} | 2023-03-21T20:54:45+00:00 |
d305b4eb353ffcd37a874e67488483fff50a4bd4 | # Dataset Card for "SGD_Hotels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Hotels | [
"region:us"
] | 2022-11-11T21:17:52+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3552843.2265793467, "num_examples": 12520}, {"name": "test", "num_bytes": 439, "num_examples": 1}], "download_size": 1494564, "dataset_size": 3553282.2265793467}} | 2023-03-21T20:55:11+00:00 |
caa9c20d19b957e45c5c37b2bb9ce26ff9c9eda0 | # Dataset Card for "SGD_Services"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Services | [
"region:us"
] | 2022-11-11T21:18:05+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3478972.4778884, "num_examples": 12956}, {"name": "test", "num_bytes": 88, "num_examples": 1}], "download_size": 1443168, "dataset_size": 3479060.4778884}} | 2023-03-21T20:55:38+00:00 |
d556035534bc479980d168dfdb0964c3b5419fb3 | # Dataset Card for "SGD_Homes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Homes | [
"region:us"
] | 2022-11-11T21:18:19+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2242529.6826529265, "num_examples": 7568}, {"name": "test", "num_bytes": 309, "num_examples": 1}], "download_size": 883348, "dataset_size": 2242838.6826529265}} | 2023-03-21T20:56:03+00:00 |
20c80c4e0b2f14f397e1bc0ee8947d40ababd2c4 | # Dataset Card for "SGD_Banks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Banks | [
"region:us"
] | 2022-11-11T21:18:32+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1001316.4280845262, "num_examples": 4400}, {"name": "test", "num_bytes": 262, "num_examples": 1}], "download_size": 339188, "dataset_size": 1001578.4280845262}} | 2023-03-21T20:56:29+00:00 |
4e0005a52c1b76e0d3f6bf9837bf9dfa48cb48d8 | # Dataset Card for "SGD_Calendar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vidhikatkoria/SGD_Calendar | [
"region:us"
] | 2022-11-11T21:18:44+00:00 | {"dataset_info": {"features": [{"name": "domain", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "act", "dtype": "int64"}, {"name": "speaker", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 647408.8420239475, "num_examples": 2588}, {"name": "test", "num_bytes": 352, "num_examples": 1}], "download_size": 235037, "dataset_size": 647760.8420239475}} | 2023-03-21T20:56:53+00:00 |
b15129889c9667380958dad75185c1d22d46b262 |
This sentiment dataset was used in the paper: John Blitzer, Mark Dredze, Fernando Pereira. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. Association of Computational Linguistics (ACL), 2007.
The author asks, if you use this data for your research or a publication, to cite the above paper as the reference for the data, and to inform him about the reuse.
The Multi-Domain Sentiment Dataset contains product reviews taken from Amazon.com from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed.
The directory contains 3 files called positive.review, negative.review and unlabeled.review. While the positive and negative files contain positive and negative reviews, these aren't necessarily the splits the authors used in the experiments. They randomly drew from the three files ignoring the file names. Each file contains a pseudo XML scheme for encoding the reviews. Most of the fields are self explanatory. The reviews have a "unique ID" field that isn't very unique. If it has two unique id fields, ignore the one containing only a number. | katossky/multi-domain-sentiment | [
"license:unknown",
"region:us"
] | 2022-11-11T21:30:46+00:00 | {"license": "unknown"} | 2022-11-11T21:45:41+00:00 |
6370e17ad58977b2f814fb28f4f5a93c26258081 | katossky/multi-domain-sentiment-books | [
"license:unknown",
"region:us"
] | 2022-11-11T22:25:29+00:00 | {"license": "unknown"} | 2022-11-12T00:33:47+00:00 |
|
147769faebba4155d65ba1f7bbef07992fc5a046 | BridgeQZH/xi_diversity | [
"license:openrail",
"region:us"
] | 2022-11-11T22:49:52+00:00 | {"license": "openrail"} | 2022-11-11T22:55:28+00:00 |
|
ca32b24bb52db3e9c5ebfa32caba60ff2a079f3d | ndorr16/ToyTruck | [
"license:gpl-3.0",
"region:us"
] | 2022-11-11T23:04:58+00:00 | {"license": "gpl-3.0"} | 2022-11-11T23:08:16+00:00 |
|
76e370b9e10ffb847b2426ac0a64f3858cc76a12 | Allenbv/Jojos-bizarre-diffusiondataset | [
"license:creativeml-openrail-m",
"region:us"
] | 2022-11-11T23:12:44+00:00 | {"license": "creativeml-openrail-m"} | 2022-11-11T23:13:23+00:00 |
|
58ccfab21f30d9a1a1ceac6c6b5a0440e587edd2 | JM138/smartwatch | [
"region:us"
] | 2022-11-11T23:59:21+00:00 | {} | 2022-11-12T00:31:57+00:00 |
|
a19e2b88393fd2ce86b61f3f74387a6aa4737cf1 | # Dataset Card for "lolita-dress-ENG"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zhangxinran/lolita-dress-ENG | [
"region:us"
] | 2022-11-12T00:24:35+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 533036535.0, "num_examples": 744}], "download_size": 530749245, "dataset_size": 533036535.0}} | 2022-11-12T00:43:03+00:00 |
bc28c1a88a57331f0cf190a777a5234a25b976bd | # Dataset Card for "stereoset_zero"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | WillHeld/stereoset_zero | [
"region:us"
] | 2022-11-12T00:49:43+00:00 | {"dataset_info": {"features": [{"name": "target", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "classes", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 900372, "num_examples": 4229}], "download_size": 311873, "dataset_size": 900372}} | 2022-11-12T00:57:23+00:00 |
b8cf69735312a73b4d5455da24faa23d4389a5c2 | Daftdroh/sisi | [
"license:other",
"region:us"
] | 2022-11-12T00:59:58+00:00 | {"license": "other"} | 2022-11-12T01:03:02+00:00 |
|
b440ccc9dfede07d020206455bb41c6df42c9f53 | # Dataset Card for "dalio-reward-model-hackathon-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Jellywibble/dalio-reward-model-hackathon-dataset | [
"region:us"
] | 2022-11-12T04:06:26+00:00 | {"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8765, "num_examples": 16}], "download_size": 6055, "dataset_size": 8765}} | 2022-11-13T17:25:41+00:00 |
37bbc9985d018c7ee582a01492c587165a043083 | # Dataset Card for "rick-and-morty-manual-captions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juliaturc/rick-and-morty-manual-captions | [
"region:us"
] | 2022-11-12T04:50:29+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11036008.0, "num_examples": 151}, {"name": "valid", "num_bytes": 925318.0, "num_examples": 16}], "download_size": 11931563, "dataset_size": 11961326.0}} | 2022-11-12T04:50:47+00:00 |
32cffc58163df4f5838a6a9635d762fde83cff9e | # Dataset Card for "dalio-conversations-hackathon-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Jellywibble/dalio-conversations-hackathon-dataset | [
"region:us"
] | 2022-11-12T05:47:33+00:00 | {"dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "scores", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 5026, "num_examples": 8}], "download_size": 8422, "dataset_size": 5026}} | 2022-11-12T23:35:14+00:00 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.