id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 β | description stringlengths 0 6.67k β | citation stringlengths 0 10.7k β | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
fruk19/ptvn_sum_cls | 2023-10-31T10:55:17.000Z | [
"region:us"
] | fruk19 | null | null | 0 | 42 | 2023-10-26T09:51:45 | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 118832924.0
num_examples: 307
- name: test
num_bytes: 45724934.0
num_examples: 115
download_size: 152076344
dataset_size: 164557858.0
---
# Dataset Card for "ptvn_sum_cls"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 467 | [
[
-0.042938232421875,
0.003490447998046875,
0.00330352783203125,
0.027618408203125,
-0.03936767578125,
-0.0126190185546875,
0.01285552978515625,
0.021881103515625,
0.058013916015625,
0.046539306640625,
-0.046630859375,
-0.04998779296875,
-0.044097900390625,
-0... |
hudssntao/test_dataset | 2023-10-27T03:27:15.000Z | [
"region:us"
] | hudssntao | null | null | 0 | 42 | 2023-10-27T02:43:19 | ---
dataset_info:
features:
- name: column1
dtype: string
- name: column2
dtype: string
splits:
- name: train
num_bytes: 40
num_examples: 2
download_size: 1227
dataset_size: 40
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "test_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 463 | [
[
-0.045196533203125,
-0.0261077880859375,
0.003826141357421875,
0.0133514404296875,
-0.009857177734375,
0.0019512176513671875,
0.0178375244140625,
-0.0021610260009765625,
0.050689697265625,
0.0228118896484375,
-0.057220458984375,
-0.047271728515625,
-0.0358276367... |
alexandrainst/wiki40b-da | 2023-10-27T19:08:09.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:da",
"license:cc-by-sa-4.0",
"region:us"
] | alexandrainst | null | null | 0 | 42 | 2023-10-27T18:47:11 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: wikidata_id
dtype: string
- name: text
dtype: string
- name: version_id
dtype: string
splits:
- name: train
num_bytes: 220855898
num_examples: 109486
- name: validation
num_bytes: 12416304
num_examples: 6173
- name: test
num_bytes: 12818380
num_examples: 6219
download_size: 150569852
dataset_size: 246090582
license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- da
pretty_name: Wiki40b-da
size_categories:
- 100K<n<1M
---
# Dataset Card for "wiki40b-da"
## Dataset Description
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
- **Size of downloaded dataset files:** 150.57 MB
- **Size of the generated dataset:** 246.09 MB
- **Total amount of disk used:** 396.66 MB
### Dataset Summary
This dataset is an upload of the Danish part of the [Wiki40b dataset](https://aclanthology.org/2020.lrec-1.297), being a cleaned version of a dump of Wikipedia.
The dataset is identical in content to [this dataset on the Hugging Face Hub](https://huggingface.co/datasets/wiki40b), but that one requires both `apache_beam`, `tensorflow` and `mwparserfromhell`, which can lead to dependency issues since these are not compatible with several newer packages.
The training, validation and test splits are the original ones.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 150.57 MB
- **Size of the generated dataset:** 246.09 MB
- **Total amount of disk used:** 396.66 MB
An example from the dataset looks as follows.
```
{
'wikidata_id': 'Q17341862',
'text': "\n_START_ARTICLE_\nΓgyptiske tekstiler\n_START_PARAGRAPH_\nTekstiler havde mange (...)",
'version_id': '9018011197452276273'
}
```
### Data Fields
The data fields are the same among all splits.
- `wikidata_id`: a `string` feature.
- `text`: a `string` feature.
- `version_id`: a `string` feature.
### Dataset Statistics
There are 109,486 samples in the training split, 6,173 samples in the validation split and 6,219 in the test split.
#### Document Length Distribution
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) uploaded it to the Hugging Face Hub.
### Licensing Information
The dataset is licensed under the [CC-BY-SA
license](https://creativecommons.org/licenses/by-sa/4.0/). | 2,790 | [
[
-0.06365966796875,
-0.037750244140625,
0.0013914108276367188,
0.0168609619140625,
-0.0163421630859375,
-0.018707275390625,
-0.0206756591796875,
-0.038116455078125,
0.03436279296875,
0.01904296875,
-0.056610107421875,
-0.046966552734375,
-0.037567138671875,
0... |
BioDEX/BioDEX-Reactions | 2023-11-01T23:10:46.000Z | [
"region:us"
] | BioDEX | null | null | 0 | 42 | 2023-11-01T20:52:32 | ---
dataset_info:
features:
- name: title
dtype: string
- name: abstract
dtype: string
- name: fulltext
dtype: string
- name: reactions
dtype: string
- name: reactions_unmerged
sequence: string
- name: pmid
dtype: string
- name: fulltext_license
dtype: string
- name: title_normalized
dtype: string
- name: issue
dtype: string
- name: pages
dtype: string
- name: journal
dtype: string
- name: authors
dtype: string
- name: pubdate
dtype: string
- name: doi
dtype: string
- name: affiliations
dtype: string
- name: medline_ta
dtype: string
- name: nlm_unique_id
dtype: string
- name: issn_linking
dtype: string
- name: country
dtype: string
- name: mesh_terms
dtype: string
- name: publication_types
dtype: string
- name: chemical_list
dtype: string
- name: keywords
dtype: string
- name: references
dtype: string
- name: delete
dtype: bool
- name: pmc
dtype: string
- name: other_id
dtype: string
- name: safetyreportids
sequence: int64
- name: fulltext_processed
dtype: string
splits:
- name: test
num_bytes: 199362395
num_examples: 4249
- name: train
num_bytes: 501649510
num_examples: 11543
- name: validation
num_bytes: 123988508
num_examples: 2886
download_size: 440721435
dataset_size: 825000413
---
# Dataset Card for "BioDEX-Reactions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,588 | [
[
-0.04705810546875,
-0.01690673828125,
0.036224365234375,
0.0092620849609375,
-0.0098724365234375,
0.01479339599609375,
0.028350830078125,
-0.0160064697265625,
0.07537841796875,
0.0176849365234375,
-0.06854248046875,
-0.0560302734375,
-0.038482666015625,
0.00... |
Abdo1Kamr/Arabic_Hadith | 2021-08-21T12:40:44.000Z | [
"region:us"
] | Abdo1Kamr | null | null | 0 | 41 | 2022-03-02T23:29:22 | # Hadith-Data-Sets
There are two files of Hadith, the first one for all `hadith With Tashkil and Without Tashkel` from the Nine Books that are 62,169 Hadith.
The second one it `Hadith pre-processing` data, which is applyed normalization and removeing stop words and lemmatization on it
<!-- ## `All Hadith Books`: All Hadith With Tashkil and Without Tashkel from the Nine Books that are 62,169 Hadith.
## `All Hadith Books_preprocessing`: All Hadith Without Tashkil which is applyed normalization and removeing stop words and lemmatization on it
-->
## Number of hadiths in whole books : 62,169
|Book Name |Number Of Hadiiths|
| ----------------------- |------------------|
|Sahih Bukhari: | 7008|
|Sahih Muslim: | 5362|
|Sunan al Tirmidhi: | 3891|
|Sunan al-Nasai: | 5662|
|Sunan Abu Dawud: | 4590|
|Sunan Ibn Maja: | 4332|
|Musnad Ahmad ibn Hanbal: | 26363|
|Maliks Muwatta: | 1594|
|Sunan al Darami: | 3367|
| 1,128 | [
[
-0.0295867919921875,
-0.0187530517578125,
0.0103302001953125,
-0.011474609375,
-0.047210693359375,
-0.011199951171875,
-0.027496337890625,
-0.001743316650390625,
0.01042938232421875,
0.05682373046875,
-0.0526123046875,
-0.05364990234375,
-0.05517578125,
0.02... |
HenryAI/KerasDeveloperGuides.txt | 2021-12-15T15:56:47.000Z | [
"region:us"
] | HenryAI | null | null | 0 | 41 | 2022-03-02T23:29:22 | Keras developer guides from https://keras.io/guides/ <br />
Formatted for input to: https://huggingface.co/blog/how-to-train | 124 | [
[
-0.030792236328125,
-0.055145263671875,
0.027496337890625,
0.03668212890625,
-0.01293182373046875,
-0.01444244384765625,
-0.00740814208984375,
-0.0166473388671875,
0.04302978515625,
0.0223236083984375,
-0.06744384765625,
-0.035888671875,
-0.028900146484375,
... |
Husain/intent-classification-en-fr | 2021-07-28T13:06:35.000Z | [
"region:us"
] | Husain | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
JIWON/nil_dataset | 2022-02-07T00:34:03.000Z | [
"region:us"
] | JIWON | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
Jack0508/eng_vi_demo | 2021-11-07T14:26:53.000Z | [
"region:us"
] | Jack0508 | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
LysandreJik/stargazers | 2021-09-26T22:47:03.000Z | [
"region:us"
] | LysandreJik | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
LysandreJik/temp-repo-valid | 2021-08-10T15:01:23.000Z | [
"region:us"
] | LysandreJik | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
LysandreJik/test-16344347220590 | 2021-10-17T01:38:42.000Z | [
"region:us"
] | LysandreJik | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
LysandreJik/test-16344347234752 | 2021-10-17T01:38:43.000Z | [
"region:us"
] | LysandreJik | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
LysandreJik/test-16344351925697 | 2021-10-17T01:46:34.000Z | [
"region:us"
] | LysandreJik | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
JustinE/Test | 2022-01-18T18:18:29.000Z | [
"region:us"
] | JustinE | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
KTH/waxholm | 2023-08-09T10:36:10.000Z | [
"task_categories:automatic-speech-recognition",
"language:sv",
"region:us"
] | KTH | The Waxholm corpus was collected in 1993 - 1994 at the department of Speech, Hearing and Music (TMH), KTH. | @article{bertenstam1995spoken,
title={Spoken dialogue data collected in the {W}axholm project},
author={Bertenstam, Johan and Blomberg, Mats and Carlson, Rolf and Elenius, Kjell and Granstr{\"o}m, Bj{\"o}rn and Gustafson, Joakim and Hunnicutt, Sheri and H{\"o}gberg, Jesper and Lindell, Roger and Neovius, Lennart and Nord, Lennart and de~Serpa-Leitao, Antonio and Str{\"o}m, Nikko},
journal={STH-QPSR, KTH},
volume={1},
pages={49--74},
year={1995}
}
@inproceedings{bertenstam1995waxholm,
title={The {W}axholm application database.},
author={Bertenstam, J and Blomberg, Mats and Carlson, Rolf and Elenius, Kjell and Granstr{\"o}m, Bj{\"o}rn and Gustafson, Joakim and Hunnicutt, Sheri and H{\"o}gberg, Jesper and Lindell, Roger and Neovius, Lennart and Nord, Lennart and de~Serpa-Leitao, Antonio and Str{\"o}m, Nikko},
booktitle={EUROSPEECH},
year={1995}
} | 0 | 41 | 2022-03-02T23:29:22 | ---
language:
- sv
task_categories:
- automatic-speech-recognition
---
# THE WAXHOLM CORPUS
The Waxholm corpus was collected in 1993 - 1994 at the department of
Speech, Hearing and Music (TMH), KTH. It is described in several
publications. Two are included in this archive. Publication of work
using the Waxholm corpus should refer to either of these. More
information on the Waxholm project can be found on the web page
http://www.speech.kth.se/waxholm/waxholm2.html
## FILE INFORMATION
### SAMPLED FILES
The .smp files contain the speech signal. The identity
of the speaker is coded by the two digits after 'fp20' in the file
name. The smp file format was developed by TMH. Recording information
is stored in a header as a 1024 byte text string. The speech signal in
the Waxholm corpus is quantised into 16 bits, 2 bytes/sample and the
byte order is big-endian (most significant byte first). The sampling
frequency is 16 kHz. Here is an example of a file header:
```
>head -9 fp2001.1.01.smp
file=samp ; file type is sampled signal
msb=first ; byte order
sftot=16000 ; sampling frequency in Hz
nchans=1 ; number of channels
preemph=no ; no signal preemphasis during recording
view=-10,10
born=/o/libhex/ad_da.h25
range=-12303,11168 ; amplitude range
=
```
### LABEL FILES
Normally, each sample file has a label file. This has been
produced in four steps. The first step was to manually enter the
orthographic text by listening. From this text a sequence of phonemes
were produced by a rule-based text-to-phoneme module. The endpoint
time positions of the phonemes were computed by an automatic alignment
program, followed by manual correction. Some of the speech files have
no label file, due to different problems in this process. These files
should not be used for training or testing.
The labels are stored in .mix files. Below is an example of the
beginning of a mix file.
```
>head -20 fp2001.1.01.smp.mix
CORRECTED: OK jesper Jesper Hogberg Thu Jun 22 13:26:26 EET 1995
AUTOLABEL: tony A. de Serpa-Leitao Mon Nov 15 13:44:30 MET 1993
Waxholm dialog. /u/wax/data/scenes/fp2001/fp2001.1.01.smp
TEXT:
jag vill }ka h{rifr}n .
J'A:+ V'IL+ "]:K'A H'[3RIFR]N.
CT 1
Labels: J'A: V'IL "]:KkA H'[3RIFR]N .
FR 11219 #J >pm #J >w jag 0.701 sec
FR 12565 $'A: >pm $'A:+ 0.785 sec
FR 13189 #V >pm #V >w vill 0.824 sec
FR 13895 $'I >pm $'I 0.868 sec
FR 14700 $L >pm $L+ 0.919 sec
```
The orthographic text representation is after the label 'TEXT:' CT is
the frame length in number of sample points. (Always = 1 in Waxholm
mix files) Each line starting with 'FR' contains up to three labels at
the phonetic, phonemic and word levels. FR is immediately followed by
the frame number of the start of the segment. Since CT = 1, FR is the
sample index in the file. If a frame duration is = 0, the label has
been judged as a non-pronounced segment and deleted by the manual
labeller, although it was generated by the text-to-phoneme or the
automatic alignment modules. Column 3 in an FR line is the phonetic
label. Initial '#' indicates word initial position. '$' indicates
other positions. The optional label '>pm' precedes the phonemic label,
which has been generated by the text-to-phoneme rules. Often, the
phonemic and the phonetic labels are identical. The optional '>w' is
followed by the identity of the word beginning at this frame. The
phoneme symbol inventory is mainly STA, used by the KTH/TMH RULSYS
system. It is specified in the included file 'sampa_latex_se.pdf'.
Some extra labels at the phonetic level have been defined.
The most common ones are:
| | |
|---------------------|------------------------------------------|
|sm | lip or tongue opening |
|p: | silent interval |
|pa | aspirative sound from breathing |
|kl | click sound |
|v | short vocalic segment between consonants |
|upper case of stops | occlusion |
|lower case of stops | burst |
The label 'Labels:' before the FR lines is a text string assembled
from the FR labels
The mix files in this archive correspond to those with the name
extension .mix.new in the original corpus. Besides a few other
corrections, the main difference is that burst segments after
retroflex stops were not labelled as retroflex in the original .mix
files ( d, t after 2D and 2T have been changed to 2d and 2t).
## REFERENCES
Bertenstam, J., Blomberg, M., Carlson, R., Elenius, K., GranstrΓΆm,
B., Gustafson, J., Hunnicutt, S., HΓΆgberg, J., Lindell, R., Neovius,
L., Nord, L., de Serpa-Leitao, A., and StrΓΆm, N.,(1995). "Spoken
dialogue data collected in the WAXHOLM project" STL-QPSR 1/1995,
KTH/TMH, Stockholm.
Bertenstam, J., Blomberg, M., Carlson, R.,
Elenius, K., GranstrΓΆm, B., Gustafson, J., Hunnicutt, S., HΓΆgberg, J.,
Lindell, R., Neovius, L., de Serpa-Leitao, A., Nord, L., & StrΓΆm,
N. (1995). The Waxholm application data-base. In Pardo, J.M. (Ed.),
Proceedings Eurospeech 1995 (pp. 833-836). Madrid.
Comments and error reports are welcome. These should be sent to:
Mats Blomberg <matsb@speech.kth.se> or Kjell Elenius <kjell@speech.kth.se> | 5,483 | [
[
-0.035491943359375,
-0.05517578125,
0.028961181640625,
0.0111541748046875,
-0.01275634765625,
-0.002376556396484375,
-0.0087890625,
-0.041656494140625,
0.051788330078125,
0.053497314453125,
-0.043121337890625,
-0.06341552734375,
-0.039886474609375,
0.0111770... |
Karavet/pioNER-Armenian-Named-Entity | 2022-10-21T16:07:06.000Z | [
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"language:hy",
"license:apache-2.0",
"region:us"
] | Karavet | null | null | 1 | 41 | 2022-03-02T23:29:22 | ---
language: [hy]
task_categories: [named-entity-recognition]
multilinguality: [monolingual]
task_ids: [named-entity-recognition]
license: [apache-2.0]
---
## Table of Contents
- [Table of Contents](#table-of-contents)
- [pioNER - named entity annotated datasets](#pioNER---named-entity-annotated-datasets)
- [Silver-standard dataset](#silver-standard-dataset)
- [Gold-standard dataset](#gold-standard-dataset)
# pioNER - named entity annotated datasets
pioNER corpus provides gold-standard and automatically generated named-entity datasets for the Armenian language.
Alongside the datasets, we release 50-, 100-, 200-, and 300-dimensional GloVe word embeddings trained on a collection of Armenian texts from Wikipedia, news, blogs, and encyclopedia.
## Silver-standard dataset
The generated corpus is automatically extracted and annotated using Armenian Wikipedia. We used a modification of [Nothman et al](https://www.researchgate.net/publication/256660013_Learning_multilingual_named_entity_recognition_from_Wikipedia) and [Sysoev and Andrianov](http://www.dialog-21.ru/media/3433/sysoevaaandrianovia.pdf) approaches to create this corpus. This approach uses links between Wikipedia articles to extract fragments of named-entity annotated texts.
The corpus is split into train and development sets.
*Table 1. Statistics for pioNER train, development and test sets*
| dataset | #tokens | #sents | annotation | texts' source |
|-------------|:--------:|:-----:|:--------:|:-----:|
| train | 130719 | 5964 | automatic | Wikipedia |
| dev | 32528 | 1491 | automatic | Wikipedia |
| test | 53606 | 2529 | manual | iLur.am |
## Gold-standard dataset
This dataset is a collection of over 250 news articles from iLur.am with manual named-entity annotation. It includes sentences from political, sports, local and world news, and is comparable in size with the test sets of other languages (Table 2).
We aim it to serve as a benchmark for future named entity recognition systems designed for the Armenian language.
The dataset contains annotations for 3 popular named entity classes:
people (PER), organizations (ORG), and locations (LOC), and is released in CoNLL03 format with IOB tagging scheme.
During annotation, we generally relied on categories and [guidelines assembled by BBN](https://catalog.ldc.upenn.edu/docs/LDC2005T33/BBN-Types-Subtypes.html) Technologies for TREC 2002 question answering track
Tokens and sentences were segmented according to the UD standards for the Armenian language from [ArmTreebank project](http://armtreebank.yerevann.com/tokenization/process/).
*Table 2. Comparison of pioNER gold-standard test set with test sets for English, Russian, Spanish and German*
| test dataset | #tokens | #LOC | #ORG | #PER |
|-------------|:--------:|:-----:|:--------:|:-----:|
| Armenian pioNER | 53606 | 1312 | 1338 | 1274 |
| Russian factRuEval-2016 | 59382 | 1239 | 1595 | 1353 |
| German CoNLL03 | 51943 | 1035 | 773 | 1195 |
| Spanish CoNLL02 | 51533 | 1084 | 1400 | 735 |
| English CoNLL03 | 46453 | 1668 | 1661 | 1671 | | 3,087 | [
[
-0.06036376953125,
-0.04425048828125,
0.00010037422180175781,
-0.0017452239990234375,
-0.0299072265625,
-0.01308441162109375,
-0.0379638671875,
-0.04168701171875,
0.0282135009765625,
0.050537109375,
-0.0184173583984375,
-0.044403076171875,
-0.04779052734375,
... |
Lenn/github-issues | 2021-11-15T10:19:39.000Z | [
"region:us"
] | Lenn | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Linda/test1111 | 2021-11-11T07:08:48.000Z | [
"region:us"
] | Linda | null | null | 0 | 41 | 2022-03-02T23:29:22 | dataset card | 12 | [
[
-0.032562255859375,
-0.0022640228271484375,
-0.01500701904296875,
-0.0007977485656738281,
-0.027496337890625,
-0.007671356201171875,
0.01263427734375,
-0.0038013458251953125,
0.0223541259765625,
0.05377197265625,
-0.043792724609375,
-0.04656982421875,
-0.0290069... |
Lumos/yahoo_hga | 2021-12-30T11:06:07.000Z | [
"region:us"
] | Lumos | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
MarianaSahagun/test | 2021-03-26T18:58:13.000Z | [
"region:us"
] | MarianaSahagun | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
MarkusDressel/cord | 2021-12-02T10:33:43.000Z | [
"region:us"
] | MarkusDressel | https://github.com/clovaai/cord | @article{park2019cord,
title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
year={2019}
} | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.014984130859375,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.04656982421875,
0.052520751953125,
0.00506591796875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
Marzipan/QA4PC | 2021-11-16T13:45:34.000Z | [
"region:us"
] | Marzipan | null | null | 0 | 41 | 2022-03-02T23:29:22 | ## QA4PC Dataset (paper: Cross-Policy Compliance Detection via Question Answering)
### Train Sets
To create training set or entailment and QA tasks, download the ShARC data from here: https://sharc-data.github.io/data.html. After that, run the script from _create_train_from_sharc.py_, by providing the path to the ShARC train and development sets.
### Evaluation Sets
#### Entailment Data
The following files contain the data for the entailment task. This includes the policy + questions, a scenario and an answer (_Yes, No, Maybe_). Each datapoint also contain the information from the ShARC dataset such as tree_id and source_url.
- __dev_entailment_qa4pc.json__
- __test_entailment_qa4pc.json__
#### QA Data
The following files contain the data for the QA task.
- __dev_sc_qa4pc.json__
- __test_sc_qa4pc.json__
The following file contains the expression tree data for the dev and test sets. Each tree includes a policy, a set of questions and a logical expression.
- __trees_dev_test_qa4pc.json__
| 1,007 | [
[
-0.033416748046875,
-0.0389404296875,
0.03814697265625,
0.013763427734375,
0.00803375244140625,
-0.00298309326171875,
0.01529693603515625,
-0.0037174224853515625,
0.0009784698486328125,
0.055694580078125,
-0.054229736328125,
-0.03387451171875,
-0.015541076660156... |
Mrleo1nid/Test_ru_dataset | 2021-09-27T02:19:08.000Z | [
"region:us"
] | Mrleo1nid | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Navigator/dodydard-marty | 2022-02-16T11:52:22.000Z | [
"region:us"
] | Navigator | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Niciu/test-squad | 2022-02-27T12:17:15.000Z | [
"region:us"
] | Niciu | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
NikolajW/NPS_nonNormalized-Cased | 2021-11-05T12:20:30.000Z | [
"region:us"
] | NikolajW | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
NishinoTSK/leishmaniaV2 | 2022-02-17T19:37:59.000Z | [
"region:us"
] | NishinoTSK | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Ofrit/tmp | 2021-02-25T11:42:23.000Z | [
"region:us"
] | Ofrit | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
PDJ107/riot-data | 2021-12-20T19:11:17.000Z | [
"region:us"
] | PDJ107 | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.021392822265625,
-0.0149688720703125,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.046539306640625,
0.052520751953125,
0.005046844482421875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.01495361328125,
-0.060333251953125,
0.03... |
PaulLerner/viquae_all_images | 2022-03-10T10:59:10.000Z | [
"region:us"
] | PaulLerner | null | null | 0 | 41 | 2022-03-02T23:29:22 | See https://github.com/PaulLerner/ViQuAE
---
license: cc-by-4.0
---
| 74 | [
[
-0.0347900390625,
-0.00936126708984375,
0.052093505859375,
0.034576416015625,
-0.038116455078125,
-0.008087158203125,
0.00572967529296875,
-0.0323486328125,
0.017791748046875,
0.049652099609375,
-0.042724609375,
-0.041015625,
-0.0229034423828125,
0.008270263... |
PaulLerner/viquae_images | 2022-02-15T17:29:53.000Z | [
"region:us"
] | PaulLerner | null | null | 0 | 41 | 2022-03-02T23:29:22 | See https://github.com/PaulLerner/ViQuAE
---
license: cc-by-4.0
---
| 74 | [
[
-0.0347900390625,
-0.00936126708984375,
0.052093505859375,
0.034576416015625,
-0.038116455078125,
-0.008087158203125,
0.00572967529296875,
-0.0323486328125,
0.017791748046875,
0.049652099609375,
-0.042724609375,
-0.041015625,
-0.0229034423828125,
0.008270263... |
Pengfei/test | 2021-09-17T14:07:13.000Z | [
"region:us"
] | Pengfei | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.021392822265625,
-0.0149688720703125,
0.057220458984375,
0.0288238525390625,
-0.03509521484375,
0.046539306640625,
0.052520751953125,
0.005046844482421875,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.01495361328125,
-0.060333251953125,
0.03... |
Pengfei/test1 | 2021-11-05T00:46:07.000Z | [
"region:us"
] | Pengfei | null | null | 0 | 41 | 2022-03-02T23:29:22 | This is the dataset | 19 | [
[
-0.01161956787109375,
0.0005512237548828125,
0.0124359130859375,
0.00841522216796875,
-0.0015802383422851562,
0.00533294677734375,
0.01702880859375,
0.018951416015625,
0.0281219482421875,
0.08074951171875,
-0.0762939453125,
-0.02069091796875,
-0.03912353515625,
... |
Pongsaky/Wiki_SCG | 2021-07-24T22:12:47.000Z | [
"region:us"
] | Pongsaky | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Pratik/Gujarati_OpenSLR | 2021-11-17T13:36:56.000Z | [
"region:us"
] | Pratik | null | null | 1 | 41 | 2022-03-02T23:29:22 | OpenSLR is a site devoted to hosting speech and language resources,
such as training corpora for speech recognition, and software related to speech recognition.
They intend to be a convenient place for anyone to put resources that they have created,
so that they can be downloaded publicly.
They aim to provide a central, hassle-free place for others to put their speech resources. see there http://www.openslr.org/contributions.html
#Supported Task
Automatic Speech Recognition
#Languages
Gujarati
Identifier: SLR78
Summary: Data set which contains recordings of native speakers of Gujarati.
Category: Speech
License: Attribution-ShareAlike 4.0 International
Downloads (use a mirror closer to you):
about.html [1.5K] (Information about the data set ) Mirrors: [China]
LICENSE [20K] (License information for the data set ) Mirrors: [China]
line_index_female.tsv [423K] (Lines recorded by the female speakers ) Mirrors: [China]
line_index_male.tsv [393K] (Lines recorded by the male speakers ) Mirrors: [China]
gu_in_female.zip [917M] (Archive containing recordings from female speakers ) Mirrors: [China]
gu_in_male.zip [825M] (Archive file recordings from male speakers ) Mirrors: [China]
About this resource:
This data set contains transcribed high-quality audio of Gujarati sentences recorded by volunteers. The data set consists of wave files, and a TSV file (line_index.tsv). The file line_index.tsv contains a anonymized FileID and the transcription of audio in the file.
The data set has been manually quality checked, but there might still be errors.
Please report any issues in the following issue tracker on GitHub. https://github.com/googlei18n/language-resources/issues
See LICENSE file for license information.
Copyright 2018, 2019 Google, Inc.
If you use this data in publications, please cite it as follows:
@inproceedings{he-etal-2020-open,
title = {{Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems}},
author = {He, Fei and Chu, Shan-Hui Cathy and Kjartansson, Oddur and Rivera, Clara and Katanova, Anna and Gutkin, Alexander and Demirsahin, Isin and Johny, Cibu and Jansche, Martin and Sarin, Supheakmungkol and Pipatsrisawat, Knot},
booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference (LREC)},
month = may,
year = {2020},
address = {Marseille, France},
publisher = {European Language Resources Association (ELRA)},
pages = {6494--6503},
url = {https://www.aclweb.org/anthology/2020.lrec-1.800},
ISBN = "{979-10-95546-34-4},
}
| 2,656 | [
[
-0.02459716796875,
-0.037384033203125,
0.012298583984375,
0.0211029052734375,
-0.029205322265625,
0.0023956298828125,
-0.0361328125,
-0.022247314453125,
0.00919342041015625,
0.0170135498046875,
-0.033721923828125,
-0.045501708984375,
-0.0307464599609375,
0.0... |
Pyjay/emotion_nl | 2022-02-14T23:26:59.000Z | [
"region:us"
] | Pyjay | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01497650146484375,
0.057098388671875,
0.028839111328125,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005046844482421875,
0.051361083984375,
0.016998291015625,
-0.05206298828125,
-0.01497650146484375,
-0.06036376953125,
0... |
Pyke/patent_abstract | 2021-08-06T05:13:33.000Z | [
"region:us"
] | Pyke | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01497650146484375,
0.057098388671875,
0.028839111328125,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005046844482421875,
0.051361083984375,
0.016998291015625,
-0.05206298828125,
-0.01497650146484375,
-0.06036376953125,
0... |
QA/abk-eng | 2021-03-09T19:12:22.000Z | [
"region:us"
] | QA | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01497650146484375,
0.057098388671875,
0.028839111328125,
-0.0350341796875,
0.046478271484375,
0.052520751953125,
0.005046844482421875,
0.051361083984375,
0.016998291015625,
-0.05206298828125,
-0.01497650146484375,
-0.06036376953125,
0... |
RBG-AI/CoRePooL | 2021-11-14T09:33:21.000Z | [
"region:us"
] | RBG-AI | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Recognai/gutenberg_spacy-ner | 2022-02-17T12:37:06.000Z | [
"region:us"
] | Recognai | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Recognai/imdb_spacy-ner | 2022-02-17T12:49:07.000Z | [
"region:us"
] | Recognai | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
RuudVelo/commonvoice_mt_8_processed | 2022-02-05T19:46:53.000Z | [
"region:us"
] | RuudVelo | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
RuudVelo/commonvoice_nl_8_processed | 2022-02-04T19:54:25.000Z | [
"region:us"
] | RuudVelo | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
SaulLu/test | 2021-08-23T12:39:00.000Z | [
"region:us"
] | SaulLu | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
SergeiGKS/wikiner_fr_job | 2021-12-14T09:48:59.000Z | [
"region:us"
] | SergeiGKS | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Shanna/Jamaica | 2021-12-10T04:21:53.000Z | [
"region:us"
] | Shanna | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
ShinyQ/PPKM_Pemerintah | 2021-10-02T03:47:28.000Z | [
"region:us"
] | ShinyQ | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
TRoboto/masc | 2021-08-16T19:34:57.000Z | [
"region:us"
] | TRoboto | null | null | 0 | 41 | 2022-03-02T23:29:22 | #MASC
The dataset will be available soon. | 42 | [
[
-0.0430908203125,
-0.0029544830322265625,
0.016632080078125,
0.027374267578125,
-0.022247314453125,
0.005130767822265625,
0.0231781005859375,
-0.020172119140625,
0.036163330078125,
0.054931640625,
-0.058319091796875,
-0.029876708984375,
-0.052276611328125,
-... |
TaahaKazi/FCE | 2021-12-02T18:21:34.000Z | [
"region:us"
] | TaahaKazi | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Terry0107/RiSAWOZ | 2021-03-21T03:16:45.000Z | [
"region:us"
] | Terry0107 | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
TestCher/Testi | 2022-02-10T09:07:54.000Z | [
"region:us"
] | TestCher | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
TheBlindBandit/SpongeNot | 2021-09-03T19:22:33.000Z | [
"region:us"
] | TheBlindBandit | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
TimTreasure4/Test | 2021-03-17T07:12:59.000Z | [
"region:us"
] | TimTreasure4 | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Trainmaster9977/957 | 2021-05-01T02:34:50.000Z | [
"region:us"
] | Trainmaster9977 | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Trainmaster9977/zbakuman | 2021-05-01T17:42:35.000Z | [
"region:us"
] | Trainmaster9977 | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Tyler/wikimatrix_collapsed | 2021-04-13T19:54:24.000Z | [
"region:us"
] | Tyler | null | null | 0 | 41 | 2022-03-02T23:29:22 | Transformation of AI.FB's Wikimatrix dataset. Combined rows containing translations of a single source sentence into one consolidated row, applying a score threshold of 1.03 to remove poor translations. | 202 | [
[
-0.04461669921875,
-0.051300048828125,
-0.01438140869140625,
0.031585693359375,
-0.0283966064453125,
-0.01934814453125,
-0.01146697998046875,
-0.0084228515625,
0.04473876953125,
0.05633544921875,
-0.0555419921875,
-0.04193115234375,
-0.0445556640625,
0.01873... |
Usin2705/test | 2021-12-07T21:02:58.000Z | [
"region:us"
] | Usin2705 | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Vishva/UniFAQ_DataSET | 2021-03-07T06:14:23.000Z | [
"region:us"
] | Vishva | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Wiedy/be | 2021-12-07T09:44:47.000Z | [
"region:us"
] | Wiedy | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Wiedy/wav2vec2-large-xls-r-300m-tr-colab | 2021-12-07T10:22:48.000Z | [
"region:us"
] | Wiedy | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Wikidepia/mc4-filter | 2021-08-03T11:19:52.000Z | [
"region:us"
] | Wikidepia | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
Wuhu0/output | 2021-10-29T08:00:02.000Z | [
"region:us"
] | Wuhu0 | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
WyrdCurt/AO4W | 2021-07-26T12:03:27.000Z | [
"region:us"
] | WyrdCurt | null | null | 0 | 41 | 2022-03-02T23:29:22 | # Archive Of Our Own Original Works (AO4W)
**Warning! Many/most of these files may be NSFW!**
Approximately 2GB of text files from Archive of Our Own; specifically, files labeled "original work" or some variation. For training fiction models. I recommend that you clean the text as needed for your purposes. | 309 | [
[
-0.018890380859375,
-0.05560302734375,
0.0482177734375,
0.0048370361328125,
-0.0221099853515625,
-0.029937744140625,
0.0364990234375,
-0.050079345703125,
0.00827789306640625,
0.09832763671875,
-0.036102294921875,
-0.01548004150390625,
-0.053009033203125,
0.0... |
XiangPan/iflytek | 2021-10-02T04:27:12.000Z | [
"region:us"
] | XiangPan | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
XiangXiang/clt | 2021-04-28T02:08:29.000Z | [
"region:us"
] | XiangXiang | null | null | 0 | 41 | 2022-03-02T23:29:22 | My new dataset | 14 | [
[
-0.04364013671875,
0.00691986083984375,
0.007198333740234375,
-0.001377105712890625,
-0.006252288818359375,
-0.004405975341796875,
0.010498046875,
0.00626373291015625,
0.051055908203125,
0.061737060546875,
-0.0246124267578125,
-0.0340576171875,
-0.0614013671875,... |
Yatoro/github_issues | 2021-11-19T01:22:57.000Z | [
"region:us"
] | Yatoro | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
abidlabs/crowdsourced-notes | 2022-01-21T15:58:14.000Z | [
"region:us"
] | abidlabs | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
abidlabs/crowdsourced-speech3 | 2022-01-21T16:12:06.000Z | [
"region:us"
] | abidlabs | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
abidlabs/crowdsourced-speech6 | 2022-01-21T17:00:29.000Z | [
"region:us"
] | abidlabs | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
abidlabs/voice-verification-adversarial-dataset | 2022-01-07T01:03:24.000Z | [
"region:us"
] | abidlabs | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
abwicke/C-B-R | 2021-03-19T16:45:29.000Z | [
"region:us"
] | abwicke | null | null | 0 | 41 | 2022-03-02T23:29:22 | https://jobs.americanbar.org/profile/cbr-watch-coming-2-america-2021-full-movie-hd-online-free/1596017/
https://jobs.americanbar.org/profile/cbr-watch-zack-snyders-justice-league-2021-full-movie-hd-online-free/1596037/
https://jobs.americanbar.org/profile/cbr-watch-tom-jerry-2021-full-movie-hd-online-free/1596053/
https://careerconnect.aamc.org/profile/cbr-watch-godzilla-vs-kong-2021-full-movie-hd-online-free/1596124/
https://careerconnect.aamc.org/profile/cbr-watch-raya-and-the-last-dragon-2021-full-movie-hd-online-free/1596135/
https://careerconnect.aamc.org/profile/cbr-watch-chaos-walking-2021-full-movie-hd-online-free/1596152/
https://careerconnect.aamc.org/profile/cbr-watch-nobody-2021-full-movie-hd-online-free/1596159/
https://careerconnect.aamc.org/profile/cbr-watch-cosmic-sin-2021-full-movie-hd-online-free/1596175/
https://careerconnect.aamc.org/profile/cbr-watch-willys-wonderland-2021-full-movie-hd-online-free/1596184/
https://careerconnect.aamc.org/profile/cbr-watch-to-all-the-boys-always-and-forever-2021-full-movie-hd-online-free/1596202/
https://careerconnect.aamc.org/profile/cbr-watch-zack-snyders-justice-league-2021-full-movie-hd-online-free/1596212/
https://careerconnect.aamc.org/profile/cbr-watch-coming-2-america-2021-full-movie-hd-online-free/1596223/
https://careerconnect.aamc.org/profile/cbr-watch-mortal-kombat-2021-full-movie-hd-online-free/1596236/
https://careerconnect.aamc.org/profile/cbr-watch-the-world-to-come-2021-full-movie-hd-online-free/1596252/
https://careerconnect.aamc.org/profile/cbr-watch-moxie-2021-full-movie-hd-online-free/1596267/
https://careerconnect.aamc.org/profile/cbr-watch-the-unholy-2021-full-movie-hd-online-free/1596272/
https://careerconnect.aamc.org/profile/cbr-watch-sas-red-notice-2021-full-movie-hd-online-free/1596280/
https://careerconnect.aamc.org/profile/cbr-watch-yes-day-2021-full-movie-hd-online-free/1596287/ | 1,894 | [
[
-0.04791259765625,
-0.0283050537109375,
0.0236663818359375,
0.020263671875,
-0.052154541015625,
0.0220489501953125,
0.041351318359375,
-0.040435791015625,
0.044708251953125,
0.038848876953125,
-0.060455322265625,
-0.0034027099609375,
-0.036590576171875,
0.00... |
abwicke/koplo | 2021-03-18T15:43:39.000Z | [
"region:us"
] | abwicke | null | null | 0 | 41 | 2022-03-02T23:29:22 | https://jobs.shrm.org/profile/free-watch-full-raya-and-the-last-dragon-2021/1589725/
https://jobs.shrm.org/profile/full-watch-raya-and-the-last-dragon-2021-hd-online-full-free-123movies/1589732/
https://jobs.shrm.org/profile/123movies-watch-raya-and-the-last-dragon-2021-hd-online-full-free-streaming/1589735/
https://jobs.shrm.org/profile/full-watch-zack-snyders-justice-league-2021-full-free/1591000/
https://jobs.shrm.org/profile/online-watch-zack-snyders-justice-league-2021-123movies-full-version-/1591028/
https://jobs.shrm.org/profile/watch-zack-snyders-justice-league-2021-online-movie-full-version-hd/1591260/
https://jobs.shrm.org/profile/full-watch-zack-snyders-justice-league-2021-hd-online-full-free-123movies/1591268/
https://jobs.shrm.org/profile/watch-zack-snyders-justice-league-2021-full-free/1591274/
https://jobs.shrm.org/profile/watch-zack-snyders-justice-league-2021-full-123movies/1591294/
https://jobs.shrm.org/profile/123movies-watch-zack-snyders-justice-league-online-2021-full-free-hd/1591301/
https://jobs.aapor.org/profile/full-watch-365-days-2020-hd-online-full-free-123movies/1592853/
https://jobs.aapor.org/profile/full-watch-army-of-the-dead-2021-hd-online-full-free-123movies/1592863/
https://jobs.aapor.org/profile/full-watch-barb-and-star-go-to-vista-del-mar-2021-hd-online-full-free-123movies/1592894/
https://jobs.aapor.org/profile/full-watch-billie-eilish-the-worlds-a-little-blurry-2021-hd-online-full-free-123movies/1592902/
https://jobs.aapor.org/profile/full-watch-black-widow-2021-hd-online-full-free-123movies/1592920/
https://jobs.aapor.org/profile/full-watch-bliss-2021-hd-online-full-free-123movies/1592926/
https://jobs.aapor.org/profile/full-watch-borat-subsequent-moviefilm-2020-hd-online-full-free-123movies/1592939/
https://jobs.aapor.org/profile/full-watch-boss-level-2021-hd-online-full-free-123movies/1592952/
https://jobs.aapor.org/profile/full-watch-chaos-walking-2021-hd-online-full-free-123movies/1592958/
https://jobs.aapor.org/profile/full-watch-cherry-2021-hd-online-full-free-123movies/1592984/
https://jobs.aapor.org/profile/full-watch-coming-2-america-2021-hd-online-full-free-123movies/1592999/
https://jobs.aapor.org/profile/full-watch-cosmic-sin-2021-hd-online-full-free-123movies/1593012/
https://jobs.aapor.org/profile/full-watch-crisis-2021-hd-online-full-free-123movies/1593017/
https://jobs.aapor.org/profile/full-watch-demon-slayer-kimetsu-no-yaiba-mugen-train-2020-hd-online-full-free-123movies/1593028/
https://jobs.aapor.org/profile/full-watch-eternals-2021-hd-online-full-free-123movies/1593038/
https://jobs.aapor.org/profile/full-watch-flora-ulysses-2021-hd-online-full-free-123movies/1593050/
https://jobs.aapor.org/profile/full-watch-girl-in-the-basement-2021-hd-online-full-free-123movies/1593056/
https://jobs.aapor.org/profile/full-watch-godzilla-vs-kong-2021-hd-online-full-free-123movies/1593071/
https://jobs.aapor.org/profile/full-watch-greenland-2020-hd-online-full-free-123movies/1593089/
https://jobs.aapor.org/profile/full-watch-i-care-a-lot-2021-hd-online-full-free-123movies/1593100/
https://jobs.aapor.org/profile/full-watch-judas-and-the-black-messiah-2021-hd-online-full-free-123movies/1593112/
https://jobs.aapor.org/profile/full-watch-land-2021-hd-online-full-free-123movies/1593128/
https://jobs.aapor.org/profile/full-watch-to-all-the-boys-always-and-forever-2021-hd-online-full-free-123movies/1592831/
https://jobs.shrm.org/profile/full-watch-zack-snyders-justice-league-2021-hd-online-movie-free-123movies/1592215/
https://jobs.shrm.org/profile/full-watch-coming-2-america-2021-hd-movie-online-free-123movies/1592252/
https://jobs.shrm.org/profile/full-watch-raya-and-the-last-dragon-2021-hd-movie-online-free-123movies/1592256/
https://jobs.shrm.org/profile/full-watch-demon-slayer-kimetsu-no-yaiba-the-movie-mugen-train-2020-hd-movie-online-free-123movies/1592270/
https://jobs.shrm.org/profile/hbo-max-watch-zack-snyders-justice-league-2021-hd-online-version/1592272/
https://jobs.aapor.org/profile/full-episodes-the-falcon-and-the-winter-soldier-season-1-episode-1-e1-online/1592374/
https://jobs.aapor.org/profile/watch-tom-jerry-2021-online-full-movie-free-123movies/1592338/
https://jobs.aapor.org/profile/hbo-max-watch-zack-snyders-justice-league-2021-hd-online-version/1592290/
https://jobs.aapor.org/profile/watch-zack-snyders-justice-league-2021-online-full-movie-hd-free/1592288/
https://jobs.aapor.org/profile/downloadhdzack-snyders-justice-league-2021-1080p-full-online-version/1592297/
https://jobs.aapor.org/profile/watch-coming-2-america-2021-online-full-movie-free-123movies/1592307/
https://jobs.aapor.org/profile/watch-zack-snyders-justice-league-2021-full-free-online-movie-here-hbomax/1592312/
https://jobs.aapor.org/profile/watch-raya-and-the-last-dragon-2021-online-full-movie-free-123movies/1592326/
https://jobs.aapor.org/profile/full-watch-tom-and-jerry-2021-full-movie-online-free-hd-stream/1592328/
https://jobs.aapor.org/profile/watch-billie-eilish-the-worlds-a-little-blurry-2021-full/1592344/
https://jobs.aapor.org/profile/watch-billie-eilish-the-worlds-a-little-blurry-2021-online-full-movie-free-123movies/1592353/
https://jobs.aapor.org/profile/watch-demon-slayer-kimetsu-no-yaiba-the-movie-mugen-train-2020-online-full-movie-free-123movies/1592363/
https://jobs.aapor.org/profile/full-watch-demon-slayer-kimetsu-no-yaiba-mugen-train-2020-full-free/1592355/
https://jobs.aapor.org/profile/watch-the-falcon-and-the-winter-soldier-season-1-e1-2021-online-free/1592389/ | 5,507 | [
[
-0.035736083984375,
-0.0037441253662109375,
0.0239715576171875,
0.003726959228515625,
-0.0465087890625,
0.00774383544921875,
0.050994873046875,
-0.05108642578125,
0.035064697265625,
0.0122833251953125,
-0.04693603515625,
-0.00438690185546875,
-0.024200439453125,... |
afasafen/newDataSet | 2022-02-07T02:27:27.000Z | [
"region:us"
] | afasafen | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
aidystark/Yt | 2022-01-20T08:49:09.000Z | [
"region:us"
] | aidystark | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01496124267578125,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.005069732666015625,
0.051361083984375,
0.0169830322265625,
-0.05206298828125,
-0.01497650146484375,
-0.060302734375,
0.03790283... |
ajmbell/test-dataset | 2021-03-22T15:55:56.000Z | [
"region:us"
] | ajmbell | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01496124267578125,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.005069732666015625,
0.051361083984375,
0.0169830322265625,
-0.05206298828125,
-0.01497650146484375,
-0.060302734375,
0.03790283... |
akhaliq/test | 2022-02-18T03:09:27.000Z | [
"region:us"
] | akhaliq | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01496124267578125,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.005069732666015625,
0.051361083984375,
0.0169830322265625,
-0.05206298828125,
-0.01497650146484375,
-0.060302734375,
0.03790283... |
bryantpwhite/Medieval_Sermons_in_French | 2022-01-05T17:34:14.000Z | [
"region:us"
] | bryantpwhite | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01496124267578125,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.005069732666015625,
0.051361083984375,
0.0169830322265625,
-0.05206298828125,
-0.01497650146484375,
-0.060302734375,
0.03790283... |
cdminix/iwslt2011 | 2021-09-21T12:17:53.000Z | [
"region:us"
] | cdminix | Both manual transcripts and ASR outputs from the IWSLT2011 speech translation evalutation campaign are often used for the related punctuation annotation task. This dataset takes care of preprocessing said transcripts and automatically inserts punctuation marks given in the manual transcripts in the ASR outputs using Levenshtein aligment. | @inproceedings{Ueffing2013,
title={Improved models for automatic punctuation prediction for spoken and written text},
author={B. Ueffing and M. Bisani and P. Vozila},
booktitle={INTERSPEECH},
year={2013}
}
@article{Federico2011,
author = {M. Federico and L. Bentivogli and M. Paul and S. StΓΌker},
year = {2011},
month = {01},
pages = {},
title = {Overview of the IWSLT 2011 Evaluation Campaign},
journal = {Proceedings of the International Workshop on Spoken Language Translation (IWSLT), San Francisco, CA}
} | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01496124267578125,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.005069732666015625,
0.051361083984375,
0.0169830322265625,
-0.05206298828125,
-0.01497650146484375,
-0.060302734375,
0.03790283... |
edfews/szdfcszdf | 2021-04-03T12:14:28.000Z | [
"region:us"
] | edfews | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01496124267578125,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.005069732666015625,
0.051361083984375,
0.0169830322265625,
-0.05206298828125,
-0.01497650146484375,
-0.060302734375,
0.03790283... |
eliza-dukim/load_klue_re | 2021-10-05T10:49:35.000Z | [
"region:us"
] | eliza-dukim | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0214080810546875,
-0.01496124267578125,
0.05718994140625,
0.02880859375,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.005069732666015625,
0.051361083984375,
0.0169830322265625,
-0.05206298828125,
-0.01497650146484375,
-0.060302734375,
0.03790283... |
ervis/qqq | 2021-02-11T18:37:48.000Z | [
"region:us"
] | ervis | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
fengzhang/fzTestDatasets | 2021-11-11T03:56:35.000Z | [
"region:us"
] | fengzhang | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
fihtrotuld/asu | 2021-09-08T01:27:31.000Z | [
"region:us"
] | fihtrotuld | null | null | 0 | 41 | 2022-03-02T23:29:22 | name: amazonRDP
on: workflow_dispatch
jobs:
build:
runs-on: windows-latest
timeout-minutes: 9999
steps:
- name: Downloading Ngrok.
run: |
Invoke-WebRequest https://raw.githubusercontent.com/romain09/AWS-RDP/main/ngrok-stable-windows-amd64.zip -OutFile ngrok.zip
Invoke-WebRequest https://raw.githubusercontent.com/romain09/AWS-RDP/main/start.bat -OutFile start.bat
- name: Extracting Ngrok Files.
run: Expand-Archive ngrok.zip
- name: Connecting to your Ngrok account.
run: .\ngrok\ngrok.exe authtoken $Env:NGROK_AUTH_TOKEN
env:
NGROK_AUTH_TOKEN: ${{ secrets.NGROK_AUTH_TOKEN }}
- name: Activating RDP access.
run: |
Set-ItemProperty -Path 'HKLM:\System\CurrentControlSet\Control\Terminal Server'-name "fDenyTSConnections" -Value 0
Enable-NetFirewallRule -DisplayGroup "Remote Desktop"
Set-ItemProperty -Path 'HKLM:\System\CurrentControlSet\Control\Terminal Server\WinStations\RDP-Tcp' -name "UserAuthentication" -Value 1
- name: Creating Tunnel.
run: Start-Process Powershell -ArgumentList '-Noexit -Command ".\ngrok\ngrok.exe tcp 3389"'
- name: Connecting to your RDP.
run: cmd /c start.bat
- name: RDP is ready!
run: |
Invoke-WebRequest https://raw.githubusercontent.com/romain09/AWS-RDP/main/loop.ps1 -OutFile loop.ps1
./loop.ps1 | 1,393 | [
[
-0.033782958984375,
-0.049835205078125,
0.027496337890625,
0.0009593963623046875,
-0.038909912109375,
0.037841796875,
0.0126953125,
-0.0267791748046875,
0.01412200927734375,
0.0440673828125,
-0.058441162109375,
-0.025146484375,
-0.056610107421875,
0.00800323... |
flax-community/code_clippy_data | 2021-07-22T22:21:46.000Z | [
"region:us"
] | flax-community | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
flax-community/norwegian-clean-dummy | 2021-07-12T11:42:18.000Z | [
"region:us"
] | flax-community | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
frtna/test | 2022-01-04T05:09:17.000Z | [
"region:us"
] | frtna | null | null | 0 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
indonesian-nlp/id_personachat | 2021-09-19T05:57:40.000Z | [
"region:us"
] | indonesian-nlp | null | null | 2 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
-0.01495361328125,
0.05718994140625,
0.0288238525390625,
-0.035064697265625,
0.046539306640625,
0.052520751953125,
0.005062103271484375,
0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
jdepoix/junit_test_completion | 2021-03-28T10:58:39.000Z | [
"region:us"
] | jdepoix | null | null | 1 | 41 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
taln-ls2n/termith-eval | 2022-09-23T07:49:04.000Z | [
"task_categories:text-generation",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:multilingual",
"size_categories:n<1K",
"language:fr",
"license:cc-by-4.0",
"region:us"
] | taln-ls2n | TermITH-Eval benchmark dataset for keyphrase extraction an generation. | @inproceedings{bougouin-etal-2016-termith,
title = "{T}erm{ITH}-Eval: a {F}rench Standard-Based Resource for Keyphrase Extraction Evaluation",
author = "Bougouin, Adrien and
Barreaux, Sabine and
Romary, Laurent and
Boudin, Florian and
Daille, B{\'e}atrice",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1304",
pages = "1924--1927",
abstract = "Keyphrase extraction is the task of finding phrases that represent the important content of a document. The main aim of keyphrase extraction is to propose textual units that represent the most important topics developed in a document. The output keyphrases of automatic keyphrase extraction methods for test documents are typically evaluated by comparing them to manually assigned reference keyphrases. Each output keyphrase is considered correct if it matches one of the reference keyphrases. However, the choice of the appropriate textual unit (keyphrase) for a topic is sometimes subjective and evaluating by exact matching underestimates the performance. This paper presents a dataset of evaluation scores assigned to automatically extracted keyphrases by human evaluators. Along with the reference keyphrases, the manual evaluations can be used to validate new evaluation measures. Indeed, an evaluation measure that is highly correlated to the manual evaluation is appropriate for the evaluation of automatic keyphrase extraction methods.",
} | 1 | 41 | 2022-04-22T09:09:23 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- fr
license: cc-by-4.0
multilinguality:
- multilingual
task_categories:
- text-mining
- text-generation
task_ids:
- keyphrase-generation
- keyphrase-extraction
size_categories:
- n<1K
pretty_name: TermITH-Eval
---
# TermITH-Eval Benchmark Dataset for Keyphrase Generation
## About
TermITH-Eval is a dataset for benchmarking keyphrase extraction and generation models.
The dataset is composed of 400 abstracts of scientific papers in French collected from the FRANCIS and PASCAL databases of the French [Institute for Scientific and Technical Information (Inist)](https://www.inist.fr/).
Keyphrases were annotated by professional indexers in an uncontrolled setting (that is, not limited to thesaurus entries).
Details about the dataset can be found in the original paper [(Bougouin et al., 2016)][bougouin-2016].
Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in [(Boudin and Gallina, 2021)][boudin-2021]. Present reference keyphrases are also ordered by their order of apparition in the concatenation of title and abstract.
Text pre-processing (tokenization) is carried out using `spacy` (`fr_core_news_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
Stemming (Snowball stemmer implementation for french provided in `nltk`) is applied before reference keyphrases are matched against the source text.
Details about the process can be found in `prmu.py`.
## Content and statistics
The dataset contains the following test split:
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
| :--------- |------------:|-----------:|-------------:|----------:|------------:|--------:|---------:|
| Test | 399 | 156.9 | 11.81 | 40.60 | 7.32 | 19.28 | 32.80 |
The following data fields are available :
- **id**: unique identifier of the document.
- **title**: title of the document.
- **abstract**: abstract of the document.
- **keyphrases**: list of reference keyphrases.
- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
- **category**: category of the document, i.e. chimie (chemistry), archeologie (archeology), linguistique (linguistics) and scienceInfo (information sciences).
## References
- (Bougouin et al., 2016) Adrien Bougouin, Sabine Barreaux, Laurent Romary, Florian Boudin, and BΓ©atrice Daille. 2016.
[TermITH-Eval: a French Standard-Based Resource for Keyphrase Extraction Evaluation][bougouin-2016].
In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1924β1927, PortoroΕΎ, Slovenia. European Language Resources Association (ELRA).Language Processing, pages 543β551, Nagoya, Japan. Asian Federation of Natural Language Processing.
- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021].
In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185β4193, Online. Association for Computational Linguistics.
[bougouin-2016]: https://aclanthology.org/L16-1304/
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/ | 3,501 | [
[
-0.017486572265625,
-0.03314208984375,
0.027740478515625,
0.01557159423828125,
-0.027191162109375,
0.0211181640625,
-0.008575439453125,
0.0016603469848632812,
0.008880615234375,
0.0303802490234375,
-0.032012939453125,
-0.056976318359375,
-0.03192138671875,
0... |
bigscience-data/roots_zh-cn_wikipedia | 2022-12-12T12:09:07.000Z | [
"language:zh",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | 19 | 41 | 2022-05-18T09:19:49 | ---
language: zh
language_bcp47:
- zh-CN
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_zh-cn_wikipedia
# wikipedia
- Dataset uid: `wikipedia`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 3.2299 % of total
- 4.2071 % of en
- 5.6773 % of ar
- 3.3416 % of fr
- 5.2815 % of es
- 12.4852 % of ca
- 0.4288 % of zh
- 0.4286 % of zh
- 5.4743 % of indic-bn
- 8.9062 % of indic-ta
- 21.3313 % of indic-te
- 4.4845 % of pt
- 4.0493 % of indic-hi
- 11.3163 % of indic-ml
- 22.5300 % of indic-ur
- 4.4902 % of vi
- 16.9916 % of indic-kn
- 24.7820 % of eu
- 11.6241 % of indic-mr
- 9.8749 % of id
- 9.3489 % of indic-pa
- 9.4767 % of indic-gu
- 24.1132 % of indic-as
- 5.3309 % of indic-or
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
#### Filters applied to: zh
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-as
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-or
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
| 3,662 | [
[
-0.045257568359375,
-0.038116455078125,
0.0232696533203125,
0.0116729736328125,
-0.01436614990234375,
-0.00582122802734375,
-0.01493072509765625,
-0.01045989990234375,
0.04534912109375,
0.0216217041015625,
-0.054168701171875,
-0.06024169921875,
-0.04458618164062... |
BeIR/fiqa-generated-queries | 2022-10-23T06:13:18.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | 2 | 41 | 2022-06-17T12:56:09 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his massΓ’β¬βenergy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German WeiΓΕΈbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | 13,988 | [
[
-0.0396728515625,
-0.03985595703125,
0.01096343994140625,
0.0036678314208984375,
0.004238128662109375,
0.00009435415267944336,
-0.008209228515625,
-0.018890380859375,
0.021697998046875,
0.00595855712890625,
-0.034332275390625,
-0.0545654296875,
-0.02638244628906... |
Shayanvsf/US_Airline_Sentiment | 2022-08-06T22:39:21.000Z | [
"region:us"
] | Shayanvsf | null | null | 0 | 41 | 2022-08-06T22:26:58 | Entry not found | 15 | [
[
-0.021392822265625,
-0.01494598388671875,
0.05718994140625,
0.028839111328125,
-0.0350341796875,
0.046539306640625,
0.052490234375,
0.00507354736328125,
0.051361083984375,
0.01702880859375,
-0.052093505859375,
-0.01494598388671875,
-0.06036376953125,
0.03790... |
RCC-MSU/collection3 | 2023-01-31T09:47:58.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ru",
"license:other",
"region:us"
] | RCC-MSU | Collection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags.
Dataset is based on collection Persons-1000 originally containing 1000 news documents labeled only with names of persons.
Additional labels were added by Valerie Mozharova and Natalia Loukachevitch.
Conversion to the IOB2 format and splitting into train, validation and test sets was done by DeepPavlov team.
For more details see https://ieeexplore.ieee.org/document/7584769 and http://labinform.ru/pub/named_entities/index.htm | @inproceedings{mozharova-loukachevitch-2016-two-stage-russian-ner,
author={Mozharova, Valerie and Loukachevitch, Natalia},
booktitle={2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT)},
title={Two-stage approach in Russian named entity recognition},
year={2016},
pages={1-6},
doi={10.1109/FRUCT.2016.7584769}} | 4 | 41 | 2022-08-23T14:03:02 | ---
annotations_creators:
- other
language:
- ru
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: Collection3
size_categories:
- 10K<n<100K
source_datasets: []
tags: []
task_categories:
- token-classification
task_ids:
- named-entity-recognition
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
splits:
- name: test
num_bytes: 935298
num_examples: 1922
- name: train
num_bytes: 4380588
num_examples: 9301
- name: validation
num_bytes: 1020711
num_examples: 2153
download_size: 878777
dataset_size: 6336597
---
# Dataset Card for Collection3
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** [Collection3 homepage](http://labinform.ru/pub/named_entities/index.htm)
- **Repository:** [Needs More Information]
- **Paper:** [Two-stage approach in Russian named entity recognition](https://ieeexplore.ieee.org/document/7584769)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Collection3 is a Russian dataset for named entity recognition annotated with LOC (location), PER (person), and ORG (organization) tags. Dataset is based on collection [Persons-1000](http://ai-center.botik.ru/Airec/index.php/ru/collections/28-persons-1000) originally containing 1000 news documents labeled only with names of persons.
Additional labels were obtained using guidelines similar to MUC-7 with web-based tool [Brat](http://brat.nlplab.org/) for collaborative text annotation.
Currently dataset contains 26K annotated named entities (11K Persons, 7K Locations and 8K Organizations).
Conversion to the IOB2 format and splitting into train, validation and test sets was done by [DeepPavlov team](http://files.deeppavlov.ai/deeppavlov_data/collection3_v2.tar.gz).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Russian
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"id": "851",
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 2, 0, 0, 0],
"tokens": ['ΠΠ»Π°Π²Π½ΡΠΉ', 'Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΠΎΡ', 'ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ³ΠΎ', 'ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ', '(', 'ΠΠ', ')', 'Π°ΠΌΠ΅ΡΠΈΠΊΠ°Π½ΡΠΊΠΎΠ³ΠΎ', 'Π²ΡΡΠΎΠΊΠΎΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ½ΠΎΠ³ΠΎ', 'Π³ΠΈΠ³Π°Π½ΡΠ°', 'Microsoft', 'Π ΡΠΉ', 'ΠΠ·Π·ΠΈ', 'ΠΏΠΎΠΊΠΈΠ΄Π°Π΅Ρ', 'ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΡ', '.']
}
```
### Data Fields
- id: a string feature.
- tokens: a list of string features.
- ner_tags: a list of classification labels (int). Full tagset with indices:
```
{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6}
```
### Data Splits
|name|train|validation|test|
|---------|----:|---------:|---:|
|Collection3|9301|2153|1922|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{mozharova-loukachevitch-2016-two-stage-russian-ner,
author={Mozharova, Valerie and Loukachevitch, Natalia},
booktitle={2016 International FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT)},
title={Two-stage approach in Russian named entity recognition},
year={2016},
pages={1-6},
doi={10.1109/FRUCT.2016.7584769}}
``` | 5,005 | [
[
-0.0360107421875,
-0.039031982421875,
0.01311492919921875,
0.011474609375,
-0.02508544921875,
0.00872039794921875,
-0.034576416015625,
-0.040374755859375,
0.023712158203125,
0.025238037109375,
-0.042022705078125,
-0.0733642578125,
-0.04559326171875,
0.013740... |
tner/multinerd | 2022-09-27T19:48:40.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:multilingual",
"size_categories:<10K",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"language:ru",
"region:us"
] | tner | [MultiNERD](https://aclanthology.org/2022.findings-naacl.60/) | @inproceedings{tedeschi-navigli-2022-multinerd,
title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
author = "Tedeschi, Simone and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.60",
doi = "10.18653/v1/2022.findings-naacl.60",
pages = "801--812",
abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
} | 5 | 41 | 2022-09-27T19:13:36 | ---
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
multilinguality:
- multilingual
size_categories:
- <10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: MultiNERD
---
# Dataset Card for "tner/multinerd"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/2022.findings-naacl.60/](https://aclanthology.org/2022.findings-naacl.60/)
- **Dataset:** MultiNERD
- **Domain:** Wikipedia, WikiNews
- **Number of Entity:** 18
### Dataset Summary
MultiNERD NER benchmark dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `PER`, `LOC`, `ORG`, `ANIM`, `BIO`, `CEL`, `DIS`, `EVE`, `FOOD`, `INST`, `MEDIA`, `PLANT`, `MYTH`, `TIME`, `VEHI`, `MISC`, `SUPER`, `PHY`
## Dataset Structure
### Data Instances
An example of `train` of `de` looks as follows.
```
{
'tokens': [ "Die", "BlΓ€tter", "des", "Huflattichs", "sind", "leicht", "mit", "den", "sehr", "Γ€hnlichen", "BlΓ€ttern", "der", "WeiΓen", "Pestwurz", "(", "\"", "Petasites", "albus", "\"", ")", "zu", "verwechseln", "." ],
'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0 ]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/multinerd/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-ORG": 5,
"I-ORG": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-PLANT": 23,
"I-PLANT": 24,
"B-MYTH": 25,
"I-MYTH": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
"B-SUPER": 31,
"I-SUPER": 32,
"B-PHY": 33,
"I-PHY": 34
}
```
### Data Splits
| language | test |
|:-----------|-------:|
| de | 156792 |
| en | 164144 |
| es | 173189 |
| fr | 176185 |
| it | 181927 |
| nl | 171711 |
| pl | 194965 |
| pt | 177565 |
| ru | 82858 |
### Citation Information
```
@inproceedings{tedeschi-navigli-2022-multinerd,
title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
author = "Tedeschi, Simone and
Navigli, Roberto",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.60",
doi = "10.18653/v1/2022.findings-naacl.60",
pages = "801--812",
abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
}
``` | 3,936 | [
[
-0.0635986328125,
-0.03778076171875,
0.00513458251953125,
0.00396728515625,
-0.0083160400390625,
0.007747650146484375,
-0.02703857421875,
-0.032257080078125,
0.045196533203125,
0.017059326171875,
-0.03857421875,
-0.060394287109375,
-0.04278564453125,
0.03961... |
bigbio/bionlp_st_2011_ge | 2022-12-22T15:43:51.000Z | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-3.0",
"region:us"
] | bigbio | The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical
documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE.
The GENIA task aims at extracting events occurring upon genes or gene products, which are typed as "Protein"
without differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components,
are not differentiated from each other, and their type is given as "Entity". | @inproceedings{10.5555/2107691.2107693,
author = {Kim, Jin-Dong and Wang, Yue and Takagi, Toshihisa and Yonezawa, Akinori},
title = {Overview of Genia Event Task in BioNLP Shared Task 2011},
year = {2011},
isbn = {9781937284091},
publisher = {Association for Computational Linguistics},
address = {USA},
abstract = {The Genia event task, a bio-molecular event extraction task,
is arranged as one of the main tasks of BioNLP Shared Task 2011.
As its second time to be arranged for community-wide focused
efforts, it aimed to measure the advance of the community since 2009,
and to evaluate generalization of the technology to full text papers.
After a 3-month system development period, 15 teams submitted their
performance results on test cases. The results show the community has
made a significant advancement in terms of both performance improvement
and generalization.},
booktitle = {Proceedings of the BioNLP Shared Task 2011 Workshop},
pages = {7β15},
numpages = {9},
location = {Portland, Oregon},
series = {BioNLP Shared Task '11}
} | 0 | 41 | 2022-11-13T22:06:52 |
---
language:
- en
bigbio_language:
- English
license: cc-by-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_3p0
pretty_name: BioNLP 2011 GE
homepage: https://sites.google.com/site/bionlpst/bionlp-shared-task-2011/genia-event-extraction-genia
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_RECOGNITION
- COREFERENCE_RESOLUTION
---
# Dataset Card for BioNLP 2011 GE
## Dataset Description
- **Homepage:** https://sites.google.com/site/bionlpst/bionlp-shared-task-2011/genia-event-extraction-genia
- **Pubmed:** True
- **Public:** True
- **Tasks:** EE,NER,COREF
The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical
documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE.
The GENIA task aims at extracting events occurring upon genes or gene products, which are typed as "Protein"
without differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components,
are not differentiated from each other, and their type is given as "Entity".
## Citation Information
```
@inproceedings{10.5555/2107691.2107693,
author = {Kim, Jin-Dong and Wang, Yue and Takagi, Toshihisa and Yonezawa, Akinori},
title = {Overview of Genia Event Task in BioNLP Shared Task 2011},
year = {2011},
isbn = {9781937284091},
publisher = {Association for Computational Linguistics},
address = {USA},
abstract = {The Genia event task, a bio-molecular event extraction task,
is arranged as one of the main tasks of BioNLP Shared Task 2011.
As its second time to be arranged for community-wide focused
efforts, it aimed to measure the advance of the community since 2009,
and to evaluate generalization of the technology to full text papers.
After a 3-month system development period, 15 teams submitted their
performance results on test cases. The results show the community has
made a significant advancement in terms of both performance improvement
and generalization.},
booktitle = {Proceedings of the BioNLP Shared Task 2011 Workshop},
pages = {7β15},
numpages = {9},
location = {Portland, Oregon},
series = {BioNLP Shared Task '11}
}
```
| 2,228 | [
[
-0.015869140625,
-0.04791259765625,
0.0245819091796875,
0.00310516357421875,
-0.0313720703125,
0.0002884864807128906,
-0.00746917724609375,
-0.05474853515625,
0.04620361328125,
0.00525665283203125,
-0.03143310546875,
-0.057464599609375,
-0.038726806640625,
0... |
Linuxdex/my-raft-submission | 2023-03-20T09:35:25.000Z | [
"benchmark:raft",
"region:us"
] | Linuxdex | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | 0 | 41 | 2023-02-12T09:19:33 | ---
benchmark: raft
type: prediction
submission_name: AG-tt
---
# RAFT submissions for my-raft-submission
## Submitting to the leaderboard
To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps:
1. Generate predictions on the unlabeled test set of each task
2. Validate the predictions are compatible with the evaluation framework
3. Push the predictions to the Hub!
See the instructions below for more details.
### Rules
1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week.
2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
### Submission file format
For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
* ID (int)
* Label (string)
See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
```python
from pathlib import Path
import pandas as pd
from collections import Counter
from datasets import load_dataset, get_dataset_config_names
tasks = get_dataset_config_names("ought/raft")
for task in tasks:
# Load dataset
raft_subset = load_dataset("ought/raft", task)
# Compute majority class over training set
counter = Counter(raft_subset["train"]["Label"])
majority_class = counter.most_common(1)[0][0]
# Load predictions file
preds = pd.read_csv(f"data/{task}/predictions.csv")
# Convert label IDs to label names
preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
# Save predictions
preds.to_csv(f"data/{task}/predictions.csv", index=False)
```
As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
```
data
βββ ade_corpus_v2
β βββ predictions.csv
β βββ task.json
βββ banking_77
β βββ predictions.csv
β βββ task.json
βββ neurips_impact_statement_risks
β βββ predictions.csv
β βββ task.json
βββ one_stop_english
β βββ predictions.csv
β βββ task.json
βββ overruling
β βββ predictions.csv
β βββ task.json
βββ semiconductor_org_types
β βββ predictions.csv
β βββ task.json
βββ systematic_review_inclusion
β βββ predictions.csv
β βββ task.json
βββ tai_safety_research
β βββ predictions.csv
β βββ task.json
βββ terms_of_service
β βββ predictions.csv
β βββ task.json
βββ tweet_eval_hate
β βββ predictions.csv
β βββ task.json
βββ twitter_complaints
βββ predictions.csv
βββ task.json
```
### Validate your submission
To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
```
python cli.py validate
```
If everything is correct, you should see the following message:
```
All submission files validated! β¨ π β¨
Now you can make a submission π€
```
### Push your submission to the Hugging Face Hub!
The final step is to commit your files and push them to the Hub:
```
python cli.py submit
```
If there are no errors, you should see the following message:
```
Submission successful! π π₯³ π
Your submission will be evaulated on Sunday 05 September 2021 β³
```
where the evaluation is run every Sunday and your results will be visible on the leaderboard. | 3,879 | [
[
-0.0309906005859375,
-0.039947509765625,
0.016998291015625,
0.037628173828125,
-0.0023632049560546875,
-0.01087188720703125,
-0.024139404296875,
-0.016143798828125,
0.0256500244140625,
0.03265380859375,
-0.0487060546875,
-0.0474853515625,
-0.045166015625,
0.... | |
tasksource/puzzte | 2023-05-31T08:43:41.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"language:en",
"license:apache-2.0",
"region:us"
] | tasksource | null | null | 1 | 41 | 2023-03-08T09:12:18 | ---
license: apache-2.0
task_ids:
- natural-language-inference
- multi-input-text-classification
task_categories:
- text-classification
language:
- en
---
https://bitbucket.org/RoxanaSz/puzzte/src/master/
```bib
@article{szomiu2021puzzle,
title={A Puzzle-Based Dataset for Natural Language Inference},
author={Szomiu, Roxana and Groza, Adrian},
journal={arXiv preprint arXiv:2112.05742},
year={2021}
}
``` | 413 | [
[
-0.004367828369140625,
-0.053253173828125,
0.0282440185546875,
0.0222930908203125,
-0.0205535888671875,
-0.00008672475814819336,
-0.0254669189453125,
-0.03546142578125,
0.039459228515625,
0.039764404296875,
-0.06536865234375,
-0.043853759765625,
-0.0398559570312... |
IlyaGusev/stihi_ru | 2023-03-20T16:01:41.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:ru",
"region:us"
] | IlyaGusev | null | null | 0 | 41 | 2023-03-16T22:05:24 | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: genre
dtype: string
- name: topic
dtype: string
- name: author
dtype: string
splits:
- name: train
num_bytes: 6029108612
num_examples: 5151050
download_size: 1892727043
dataset_size: 6029108612
task_categories:
- text-generation
language:
- ru
size_categories:
- 1M<n<10M
---
# Stihi.ru dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Description](#description)
- [Usage](#usage)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Description
**Summary:** A subset if [Taiga](https://tatianashavrina.github.io/taiga_site/), uploaded here for convenience. Additional cleaning was performed.
**Script:** [create_stihi.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/create_stihi.py)
**Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu)
**Languages:** Russian.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Dataset iteration:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/stihi_ru', split="train", streaming=True)
for example in dataset:
print(example["text"])
```
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible. | 1,503 | [
[
-0.00004571676254272461,
-0.01751708984375,
0.0200042724609375,
0.004344940185546875,
-0.033721923828125,
-0.01134490966796875,
-0.019378662109375,
-0.01062774658203125,
0.025146484375,
0.0311279296875,
-0.049468994140625,
-0.054412841796875,
-0.03790283203125,
... |
s-nlp/en_paradetox_content | 2023-09-08T08:38:03.000Z | [
"task_categories:text-classification",
"language:en",
"license:openrail++",
"region:us"
] | s-nlp | null | null | 0 | 41 | 2023-03-24T11:07:04 | ---
license: openrail++
task_categories:
- text-classification
language:
- en
---
# ParaDetox: Detoxification with Parallel Data (English). Content Task Results
This repository contains information about **Content Task** markup from [English Paradetox dataset](https://huggingface.co/datasets/s-nlp/paradetox) collection pipeline.
The original paper ["ParaDetox: Detoxification with Parallel Data"](https://aclanthology.org/2022.acl-long.469/) was presented at ACL 2022 main conference.
## ParaDetox Collection Pipeline
The ParaDetox Dataset collection was done via [Yandex.Toloka](https://toloka.yandex.com/) crowdsource platform. The collection was done in three steps:
* *Task 1:* **Generation of Paraphrases**: The first crowdsourcing task asks users to eliminate toxicity in a given sentence while keeping the content.
* *Task 2:* **Content Preservation Check**: We show users the generated paraphrases along with their original variants and ask them to indicate if they have close meanings.
* *Task 3:* **Toxicity Check**: Finally, we check if the workers succeeded in removing toxicity.
Specifically this repo contains the results of **Task 2: Content Preservation Check**. Here, the samples with markup confidence >= 90 are present. One text in the pair is toxic, another -- its non-toxic paraphrase (should be).
Totally, datasets contains 32,317 pairs. Among them, the minor part is negative examples (4,562 pairs).
## Citation
```
@inproceedings{logacheva-etal-2022-paradetox,
title = "{P}ara{D}etox: Detoxification with Parallel Data",
author = "Logacheva, Varvara and
Dementieva, Daryna and
Ustyantsev, Sergey and
Moskovskiy, Daniil and
Dale, David and
Krotova, Irina and
Semenov, Nikita and
Panchenko, Alexander",
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.469",
pages = "6804--6818",
abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.",
}
```
## Contacts
For any questions, please contact: Daryna Dementieva (dardem96@gmail.com) | 3,284 | [
[
-0.0069732666015625,
-0.028717041015625,
0.05084228515625,
0.01534271240234375,
-0.0218048095703125,
-0.0037078857421875,
-0.005367279052734375,
0.0014715194702148438,
0.015289306640625,
0.06085205078125,
-0.0268707275390625,
-0.07080078125,
-0.040008544921875,
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.