cardiffnlp/twitter-roberta-base-topic-sentiment-latest
This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 and finetuned for sentiment analysis (target based) on the TweetSentiment dataset of SuperTweetEval. The original Twitter-based RoBERTa model can be found here.
Labels
Example
from transformers import pipeline
text= 'If I make a game as a #windows10 Universal App. Will #xboxone owners be able to download and play it in November? @user @microsoft'
target = "@microsoft"
text_input = f"{text} </s> {target}"
pipe = pipeline('text-classification', model="cardiffnlp/twitter-roberta-base-topic-sentiment-latest")
pipe(text)
>> [{'label': 'negative or neutral', 'score': 0.9601162672042847}]
Citation Information
Please cite the reference paper if you use this model.
@inproceedings{antypas2023supertweeteval,
title={SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research},
author={Dimosthenis Antypas and Asahi Ushio and Francesco Barbieri and Leonardo Neves and Kiamehr Rezaee and Luis Espinosa-Anke and Jiaxin Pei and Jose Camacho-Collados},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
year={2023}
}