Datasets:
index int32 1.47B 1.97B | hashtag stringlengths 1 41 | segmentation stringlengths 1 50 ⌀ | gold_position int32 0 19 ⌀ | rank sequence |
|---|---|---|---|---|
1,467,856,821 | therapyfail | therapy fail | 8 | {
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1,467,895,481 | gayforpeavy | gay for peavy | 4 | {
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1,467,918,728 | camerafail | camera fail | 8 | {
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1,468,099,958 | fail | fail | 0 | {
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1,468,113,109 | KrispyKreme | Krispy Kreme | 7 | {
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1,468,156,574 | fail | fail | 0 | {
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1,468,243,138 | Kulula | Kulula | 3 | {
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1,468,244,914 | surface | surface | 8 | {
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1,468,293,310 | rails | rails | 0 | {
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1,468,324,687 | stuckrecord | stuck record | 4 | {
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1,468,360,001 | twitteriffic | twit teriffic | 6 | {
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1,468,374,080 | startrek | star trek | 12 | {
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1,468,375,888 | earthquake | earthquake | 19 | {
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1,468,422,938 | error | error | 7 | {
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1,468,426,052 | tracy | tracy | 6 | {
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1,468,449,262 | Earthquake | Earthquake | 19 | {
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1,468,472,365 | fantasysurfer | fantasy surfer | 14 | {
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1,468,484,103 | titsuptuesday | titsup tuesday | 1 | {
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1,468,506,889 | House | House | 1 | {
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1,468,518,616 | babypunch | baby punch | 18 | {
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1,468,588,836 | italy | italy | 1 | {
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1,468,598,198 | AutomationAtaCost | Automation At a Cost | 10 | {
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1,468,639,063 | rockbox | rock box | 6 | {
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1,468,645,098 | goodlife | good life | 14 | {
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1,468,653,290 | Ambients | Ambients | 5 | {
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1,468,659,365 | theinbetweeners | the in betweeners | 13 | {
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1,468,674,937 | Summers | Summers | 0 | {
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1,468,690,477 | spotify | spotify | 0 | {
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1,468,929,947 | semantic | semantic | 2 | {
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1,468,938,101 | Virgin | Virgin | 6 | {
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1,468,947,202 | Easter | Easter | 12 | {
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1,468,991,999 | barcodes | barcodes | 19 | {
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1,469,006,230 | nickdarcy | nick darcy | 5 | {
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1,469,049,688 | house | house | 1 | {
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1,469,340,761 | Mesh09 | Mesh 09 | 8 | {
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1,469,363,074 | House | House | 1 | {
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1,469,501,005 | ottawa | ottawa | 7 | {
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1,469,702,025 | IVAA09 | IVAA 09 | 7 | {
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1,469,835,705 | photog | photog | 2 | {
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1,469,889,030 | bluepeter | blue peter | 8 | {
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1,469,947,123 | WiiFit | Wii Fit | 0 | {
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1,469,968,927 | cricket | cricket | 2 | {
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1,470,126,516 | Lahore | Lahore | 7 | {
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1,470,181,979 | Heroes | Heroes | 9 | {
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1,548,344,054 | frinightfever | fri night fever | 4 | {
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1,548,497,981 | Angels | Angels | 10 | {
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1,548,524,150 | ASOT400 | ASOT 400 | 4 | {
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1,548,556,589 | howling | howling | 10 | {
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1,548,558,575 | ASOT400 | ASOT 400 | 4 | {
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1,548,606,971 | gamearena | game arena | 10 | {
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"in dones... |
1,555,987,122 | barcamphanoi | barcamp hanoi | 6 | {
"position": [
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20
],
"candidate": [
"bar camp ha no i",
"bar camp hano i",
"barca mp hanoi",
"barc a mph a noi",
"bar cam ph a noi",
"ba r cam... |
YAML Metadata Warning: The task_categories "structure-prediction" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Dataset Card for Test-Stanford
Dataset Summary
Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al..
Languages
English
Dataset Structure
Data Instances
{
"index": 1467856821,
"hashtag": "therapyfail",
"segmentation": "therapy fail",
"gold_position": 8,
"rank": {
"position": [
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20
],
"candidate": [
"therap y fail",
"the rap y fail",
"t her apy fail",
"the rap yfail",
"t he rap y fail",
"thera py fail",
"ther apy fail",
"th era py fail",
"therapy fail",
"therapy fai l",
"the r apy fail",
"the rapyfa il",
"the rapy fail",
"t herapy fail",
"the rapyfail",
"therapy f ai l",
"therapy fa il",
"the rapyf a il",
"therapy f ail",
"the ra py fail"
]
}
}
Data Fields
index: a numerical index annotated by Kodali et al..hashtag: the original hashtag.segmentation: the gold segmentation for the hashtag.gold_position: position of the gold segmentation on thesegmentationfield inside therank.rank: Rank of each candidate selected by a baseline word segmenter ( Segmentations Seeder Module ).
Dataset Creation
All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields:
hashtagandsegmentationoridentifierandsegmentation.The only difference between
hashtagandsegmentationor betweenidentifierandsegmentationare the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as
_,:,~).If there are any annotations for named entity recognition and other token classification tasks, they are given in a
spansfield.
Additional Information
Citation Information
@misc{bansal2015deep,
title={Towards Deep Semantic Analysis Of Hashtags},
author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
year={2015},
eprint={1501.03210},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
Contributions
This dataset was added by @ruanchaves while developing the hashformers library.
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