Datasets:

cardiffnlp
/

super_tweeteval

Tasks:

Text Classification

Token Classification

Question Answering

Modalities:

Formats:

Sub-tasks:

topic-classification

named-entity-recognition

Languages:

Size:

ArXiv:

Tags:

super_tweet_eval

natural language understanding

Libraries:

License:

Dataset card Viewer Files Files and versions Community

asahi417 commited on Jun 1, 2023

Commit

aea4d69

•

1 Parent(s): 4a08b3e

fix dataset

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -124,6 +124,7 @@ The data fields are the same among all splits.
 | tweet_topic      | multi-label classification         | 4,585 / 573 / 1,679     |
 | tweet_ner7       | sequence labeling                  | 4,616 / 576 / 2,807     |
 | tweet_qa         | generation                         | 9,489 / 1,086 / 1,203    |
 | tweet_intimacy   | regression on a single text        | 1,191 / 396 / 396      |
 | tweet_similarity | regression on two texts            | 450 / 100 / 450       |
 | tempo_wic        | binary classification on two texts | 1,427 / 395 / 1,472     |
@@ -131,7 +132,7 @@ The data fields are the same among all splits.
 | tweet_emoji      | multi-class classification         | 50,000 / 5,000 / 50,000 |
 | tweet_sentiment  | ABSA on a five-pointscale          | 26,632 / 4,000 / 12,379  |
 | tweet_nerd       | binary classification              | * / 407 / *           |
-| tweet_emotion       | multi-label classification      | 6,838 / 886 / 3,259      |
 ## Evaluation Metrics

 | tweet_topic      | multi-label classification         | 4,585 / 573 / 1,679     |
 | tweet_ner7       | sequence labeling                  | 4,616 / 576 / 2,807     |
 | tweet_qa         | generation                         | 9,489 / 1,086 / 1,203    |
+| tweet_qg         | generation                         | 9,489 / 1,086 / 1,203    |
 | tweet_intimacy   | regression on a single text        | 1,191 / 396 / 396      |
 | tweet_similarity | regression on two texts            | 450 / 100 / 450       |
 | tempo_wic        | binary classification on two texts | 1,427 / 395 / 1,472     |
 | tweet_emoji      | multi-class classification         | 50,000 / 5,000 / 50,000 |
 | tweet_sentiment  | ABSA on a five-pointscale          | 26,632 / 4,000 / 12,379  |
 | tweet_nerd       | binary classification              | * / 407 / *           |
+| tweet_emotion    | multi-label classification      | 6,838 / 886 / 3,259      |
 ## Evaluation Metrics