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73cc8d2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from __future__ import annotations
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks import AbsTaskClassification
class TweetSentimentExtractionClassification(AbsTaskClassification):
metadata = TaskMetadata(
name="TweetSentimentExtractionClassification",
description="",
reference="https://www.kaggle.com/competitions/tweet-sentiment-extraction/overview",
dataset={
"path": "mteb/tweet_sentiment_extraction",
"revision": "d604517c81ca91fe16a244d1248fc021f9ecee7a",
},
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs=["eng-Latn"],
main_score="accuracy",
date=(
"2020-01-01",
"2020-12-31",
), # Estimated range for the collection of tweets
form=["written"],
domains=["Social"],
task_subtypes=["Sentiment/Hate speech"],
license="Not specified",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@misc{tweet-sentiment-extraction,
author = {Maggie, Phil Culliton, Wei Chen},
title = {Tweet Sentiment Extraction},
publisher = {Kaggle},
year = {2020},
url = {https://kaggle.com/competitions/tweet-sentiment-extraction}
}""",
n_samples={"test": 3534},
avg_character_length={"test": 67.8},
)
@property
def metadata_dict(self) -> dict[str, str]:
metadata_dict = dict(self.metadata)
metadata_dict["n_experiments"] = 10
metadata_dict["samples_per_label"] = 32
return metadata_dict
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