Datasets:
text
stringlengths 1
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| labels
sequence | dataset_name
class label 9
classes | aspect
class label 3
classes |
---|---|---|---|
[NAME] is da man. A gangsta mathematician who had the guts to seduce the most beautiful Spartan woman of her time. | [
"admiration",
"neutral"
] | 0go_emotion
| 1sentiment
|
Reminds me of that iCarly episode for the recycle project in science class with the nuclear reactor | [
"neutral"
] | 0go_emotion
| 1sentiment
|
No, the ECJ made unilateral A50 revocation subject to good faith use, not as a tactical tool to gain a better deal. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
That’s because she went on to miss the lay up | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Hey! Anyone have the DL on this MLM ? Would love some links to send a victim!! | [
"love",
"neutral"
] | 0go_emotion
| 1sentiment
|
Available 24/7? You could put a ring on it and still not get that kind of service. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Why? I would rather the players get the money than the owners | [
"neutral"
] | 0go_emotion
| 1sentiment
|
*Trust the plan* | [
"approval",
"neutral"
] | 0go_emotion
| 1sentiment
|
So much tininess! | [
"neutral"
] | 0go_emotion
| 1sentiment
|
I mean if she wants to create change has she considered changing her whole personality and just being quiet | [
"neutral"
] | 0go_emotion
| 1sentiment
|
So he watches a movie documenting how a band became mainstream and complains about them being mainstream. O-k | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Yup, even if they had a very rough life... Nobody ages thaaaaaat badly. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
If you weren't connected at the head you'd be okay, until the other head started to decompose. You'd probably die of septic shock | [
"neutral"
] | 0go_emotion
| 1sentiment
|
"Don't be stupid and be a smarty, come and join the Nazi party !" | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Read: A Billion Wicked Thoughts: What the Internet Tells Us About Sexual Relationships It will reveal all | [
"neutral"
] | 0go_emotion
| 1sentiment
|
its not even illegal for like siblings and parents/children to have sex as long as they are adults and consenting when the relationship begins. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
I’m not arguing for it. I just think the punishment doesn’t fit the crime. | [
"realization",
"neutral"
] | 0go_emotion
| 1sentiment
|
That might be the first accurate thing you’ve said today! (You’re merely a couple of decades late.) | [
"neutral"
] | 0go_emotion
| 1sentiment
|
[NAME] redeemed herself!! | [
"neutral"
] | 0go_emotion
| 1sentiment
|
[NAME]: "S that D! Shut it down!" | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Me. I really needed to see this. ☹️ | [
"sadness",
"neutral"
] | 0go_emotion
| 1sentiment
|
Reddit is being way too forgiving of [NAME] for some reason, it's an odd nerve that I've touched here apparently but keep the downvotes coming. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
I know a lot of people around my rural area who did. These are people my age ~28 sharing this tigger the libs crap. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
They can choose not to be abused by refusing to work such a shitty job | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Cut into this guy's hair, [NAME] is ftfy | [
"neutral"
] | 0go_emotion
| 1sentiment
|
[NAME] is creepy about nearly everything, but the way she is so possessive over [NAME] is super uncomfortable. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Needs funny captions. | [
"desire",
"neutral"
] | 0go_emotion
| 1sentiment
|
It's a shame he didn't trust you enough to believe you, but instead believed his own assessment of what he was worth. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Shouldn't have drank water from his cup. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
"brush my back again, I dare you, I double dare you." | [
"neutral"
] | 0go_emotion
| 1sentiment
|
begone tree hugger over there. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
You forgot shinobi | [
"realization",
"neutral"
] | 0go_emotion
| 1sentiment
|
The end was kinda meeeehhhh. Like Season 9 AMIRITE THX | [
"neutral"
] | 0go_emotion
| 1sentiment
|
None of it will happen Just wait for them to yank it away | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Yeah there's a lot of innately absurd conclusions this guy's drawing here | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Ha keep him on his toes | [
"neutral"
] | 0go_emotion
| 1sentiment
|
She does't even go here | [
"neutral"
] | 0go_emotion
| 1sentiment
|
I haven't gone to someone to talk about it. I really want to, though. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
No, because I don't have to. This is an "Everyone else" problem. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
No problem, it's never easy to be in this situation. It's best to end things neatly before you end up resenting each other | [
"neutral"
] | 0go_emotion
| 1sentiment
|
"[NAME]" Still love [NAME], and while the actual [NAME] was cool at first, she's gotten pretty "meh" to me. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
So can [NAME] | [
"neutral"
] | 0go_emotion
| 1sentiment
|
The real reason [NAME] hates pewdiepie | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Ok, but can you explain why you story doesn’t fit the video? | [
"neutral"
] | 0go_emotion
| 1sentiment
|
the Bulls turn cap space into cash money. Doesn't help or hurt the team, but makes the owner happy | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Translation: "I don't think restaurant employees should be allowed to express frustration under any circumstances." | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Yeah I was just trying to save some money and I've been alright with G2A before | [
"desire",
"neutral"
] | 0go_emotion
| 1sentiment
|
Oh yeah that | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Well damn I’m going to the Buckingham today | [
"neutral"
] | 0go_emotion
| 1sentiment
|
*Day after day, ordinary people become heroes through extraordinary and selfless actions to help their neighbors.* -[NAME] | [
"neutral"
] | 0go_emotion
| 1sentiment
|
>just because I miss [NAME] and [NAME], who played them. You forgot to add the rest of that sentence | [
"sadness",
"neutral"
] | 0go_emotion
| 1sentiment
|
Anyone who complains about being a loser is a loser. | [
"annoyance",
"neutral"
] | 0go_emotion
| 1sentiment
|
Yeah very true | [
"approval",
"neutral"
] | 0go_emotion
| 1sentiment
|
If you are not worried about stranger danger I could probably give you a lift. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Yeah, look at the publicity for just that. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
With that logic, women shouldn't be able to drive cars considering they are more prone to accidents/wrecks. Would you agree? | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Yeah, could have easily been 7-5 or 7-6 or 8-7... | [
"neutral"
] | 0go_emotion
| 1sentiment
|
People do realize that getting an ad declined for the Super Bowl is an actual advertising strategy, right? | [
"neutral"
] | 0go_emotion
| 1sentiment
|
You absolutely should not tell her. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
[NAME] an Angel now. ⭐️ | [
"neutral"
] | 0go_emotion
| 1sentiment
|
And Tranna for Toronto. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
He looks like bob saget | [
"neutral"
] | 0go_emotion
| 1sentiment
|
I work at an R1 university, with most of my team composed of CS/CE undergrad students. Yes, I've known many... | [
"approval",
"neutral"
] | 0go_emotion
| 1sentiment
|
ahh sweet man seems like i have more luck at night like 6 and after | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Check the frame (FPS) limit option in the advanced graphic options menu. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
We got a room full if potheads, were gonna need back up.” “I’M ON MY WAY!” | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Google Tom Green sandwiches. It isn't the kind of thing you explain. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
As wholesome as that is... How is that a confession? That's casual discussion. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Good: Beat OSU Better: Humiliate OSU Best: Embarrass OSU so badly that they lose recruits to us after the game | [
"neutral"
] | 0go_emotion
| 1sentiment
|
I'd say that the vast majority of the population is and was rightly looked on as horrific. Christmas music makes me feel like a real [NAME]. | [
"disappointment",
"neutral"
] | 0go_emotion
| 1sentiment
|
Waddle like your life depends on it because it might. Ice is dangerous. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Plus like why would they go for the head? You’d have to break both of his legs to stop [NAME]. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
We literally share a parking lot with a bank and I get people asking me for change all. The. Time. I struggle. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Can anyone tell me what happened to [NAME]? | [
"neutral"
] | 0go_emotion
| 1sentiment
|
It more means nuxia needs help. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
It was a commercial. Do a lot of conservatives have this problem? | [
"neutral"
] | 0go_emotion
| 1sentiment
|
“Keep a place for me” “I’ll sleep between y’all it’s nothin” | [
"neutral"
] | 0go_emotion
| 1sentiment
|
You’re now banned from /r/starwars and some YouTube channels | [
"neutral"
] | 0go_emotion
| 1sentiment
|
“Screaming outrage” you say? Project much? | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Putting the opposite outcome of what [NAME] says is a close second imo | [
"neutral"
] | 0go_emotion
| 1sentiment
|
[NAME] holding grudges | [
"neutral"
] | 0go_emotion
| 1sentiment
|
He said “Guess he didn’t get the victory [NAME] :(“ | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Nothing gets past you! | [
"neutral"
] | 0go_emotion
| 1sentiment
|
That's what they're for! I wouldn't use it for anything else. And make sure you don't have plans 6-10 hours from taking it... | [
"neutral"
] | 0go_emotion
| 1sentiment
|
You know [waves hand] Everyone. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Cruz Azul let go a young [NAME] because they had [NAME] higher on their goalkeeper pick lol | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Nah, I knock on the door from inside, or the wall, or tap the floor, seems easier | [
"disapproval",
"neutral"
] | 0go_emotion
| 1sentiment
|
Yeah they paid for my dinner but still ugh | [
"neutral"
] | 0go_emotion
| 1sentiment
|
3/10 bad troll | [
"neutral"
] | 0go_emotion
| 1sentiment
|
If your significant other had sex with someone after getting drunk, you wouldn't consider that cheating? | [
"confusion",
"neutral"
] | 0go_emotion
| 1sentiment
|
Well that's hardly this other woman's problem. You come across as a bitter, immature incel, so maybe sort your attitude out before trying to attract anyone. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Watch it and find out. Go in with a fresh mind. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
A) at first it looked like sex doll B) aren't the staples gonna sting you in the ass at some point? | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Those didn't produce any relevant examples. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
The trouble is that they are, in fact, very devout [NAME]. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
As lad she comin | [
"neutral"
] | 0go_emotion
| 1sentiment
|
"You might feel some slight discomfort" | [
"neutral"
] | 0go_emotion
| 1sentiment
|
If the anime really ends that way there’s no way I see [NAME] and [NAME] get back together. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
“I wish there was a way to know you're in the good old days before you've actually left them.” | [
"desire",
"neutral"
] | 0go_emotion
| 1sentiment
|
My 35kg ball of quivering nerves, which I am currently wearing like a tea cosy, would like to register an objection. | [
"neutral"
] | 0go_emotion
| 1sentiment
|
Universal Text Classification Dataset (UTCD)
Load dataset
from datasets import load_dataset
dataset = load_dataset('claritylab/utcd', name='in-domain')
Description
UTCD is a curated compilation of 18 datasets revised for Zero-shot Text Classification spanning 3 aspect categories of Sentiment, Intent/Dialogue, and Topic classification. UTCD focuses on the task of zero-shot text classification where the candidate labels are descriptive of the text being classified. TUTCD consists of ~ 6M/800K train/test examples.
UTCD was introduced in the Findings of ACL'23 Paper Label Agnostic Pre-training for Zero-shot Text Classification by Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang and Jason Mars. Project Homepage.
UTCD Datasets & Principles:
In order to make NLP models more broadly useful, zero-shot techniques need to be capable of label, domain & aspect transfer. As such, in the construction of UTCD we enforce the following principles:
Textual labels: In UTCD, we mandate the use of textual labels. While numerical label values are often used in classification tasks, descriptive textual labels such as those present in the datasets across UTCD enable the development of techniques that can leverage the class name which is instrumental in providing zero-shot support. As such, for each of the compiled datasets, labels are standardized such that the labels are descriptive of the text in natural language.
Diverse domains and Sequence lengths: In addition to broad coverage of aspects, UTCD compiles diverse data across several domains such as Banking, Finance, Legal, etc each comprising varied length sequences (long and short). The datasets are listed above.
Sentiment
- GoEmotions introduced in GoEmotions: A Dataset of Fine-Grained Emotions
- TweetEval introduced in TWEETEVAL: Unified Benchmark and Comparative Evaluation for Tweet Classification (Sentiment subset)
- Emotion introduced in CARER: Contextualized Affect Representations for Emotion Recognition
- Amazon Polarity introduced in Character-level Convolutional Networks for Text Classification
- Finance Phrasebank introduced in Good debt or bad debt: Detecting semantic orientations in economic texts
- Yelp introduced in Character-level Convolutional Networks for Text Classification
Intent/Dialogue
- Schema-Guided Dialogue introduced in Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset
- Clinc-150 introduced in An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
- SLURP SLU introduced in SLURP: A Spoken Language Understanding Resource Package
- Banking77 introduced in Efficient Intent Detection with Dual Sentence Encoders
- Snips introduced in Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
- NLU Evaluation introduced in Benchmarking Natural Language Understanding Services for building Conversational Agents
Topic
- AG News introduced in Character-level Convolutional Networks for Text Classification
- DBpedia 14 introduced in DBpedia: A Nucleus for a Web of Open Data
- Yahoo Answer Topics introduced in Character-level Convolutional Networks for Text Classification
- MultiEurlex introduced in MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer
- BigPatent introduced in BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
- Consumer Finance introduced in Consumer Complaint Database
Structure
Data Samples
Each dataset sample contains the text, the label encoded as an integer, and the dataset name encoded as an integer.
{
'text': "My favourite food is anything I didn't have to cook myself.",
'labels': [215],
'dataset_name': 0
}
Datasets Contained
The UTCD dataset contains 18 datasets, 9 in-domain
, 9 out-of-domain
, spanning 3 aspects: sentiment
, intent
and topic
.
Below are statistics on the datasets.
In-Domain Datasets
Dataset | Aspect | #Samples in Train/Test | #labels | average #token in text in Train/Test |
---|---|---|---|---|
GoEmotions | sentiment | 43K/5.4K | 28 | 12/12 |
TweetEval | sentiment | 45K/12K | 3 | 19/14 |
Emotion | sentiment | 16K/2K | 6 | 17/17 |
SGD | intent | 16K/4.2K | 26 | 8/9 |
Clinc-150 | intent | 15K/4.5K | 150 | 8/8 |
SLURP | intent | 12K/2.6K | 75 | 7/7 |
AG News | topic | 120K7.6K | 4 | 38/37 |
DBpedia | topic | 560K/70K | 14 | 45/45 |
Yahoo | topic | 1.4M/60K | 10 | 10/10 |
Out-of-Domain Datasets
Dataset | Aspect | #Samples in Train/Test | #labels | average #token in text |
---|---|---|---|---|
Amazon Polarity | sentiment | 3.6M/400K | 2 | 71/71 |
Financial Phrase Bank | sentiment | 1.8K/453 | 3 | 19/19 |
Yelp | sentiment | 650K/50K | 3 | 128/128 |
Banking77 | intent | 10K/3.1K | 77 | 11/10 |
SNIPS | intent | 14K/697 | 7 | 8/8 |
NLU Eval | intent | 21K/5.2K | 68 | 7/7 |
MultiEURLEX | topic | 55K/5K | 21 | 1198/1853 |
Big Patent | topic | 25K/5K | 9 | 2872/2892 |
Consumer Finance | topic | 630K/160K | 18 | 190/189 |
Configurations
The in-domain
and out-of-domain
configurations has 2 splits: train
and test
.
The aspect-normalized configurations (aspect-normalized-in-domain
, aspect-normalized-out-of-domain
) has 3 splits: train
, validation
and test
.
Below are statistics on the configuration splits.
In-Domain Configuration
Split | #samples |
---|---|
Train | 2,192,703 |
Test | 168,365 |
Out-of-Domain Configuration
Split | #samples |
---|---|
Train | 4,996,673 |
Test | 625,911 |
Aspect-Normalized In-Domain Configuration
Split | #samples |
---|---|
Train | 115,127 |
Validation | 12,806 |
Test | 168,365 |
Aspect-Normalized Out-of-Domain Configuration
Split | #samples |
---|---|
Train | 119,167 |
Validation | 13,263 |
Test | 625,911 |
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