cardiffnlp/twitter-roberta-base-emotion-latest
This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 and finetuned for emotion classification (multilabel classification) on the TweetEmotion dataset of SuperTweetEval. The original Twitter-based RoBERTa model can be found here.
Labels
Example
from transformers import pipeline
text= "@user it also helps that the majority of NFL coaching is inept. Some of Bill O'Brien's play calling was wow, ! #GOPATS"
pipe = pipeline('text-classification', model="cardiffnlp/twitter-roberta-base-emotion-latest", return_all_scores=True)
predictions = pipe(text)[0]
predictions = [x for x in predictions if x['score'] > 0.5]
predictions
>> [{'label': 'anger', 'score': 0.8713036775588989},
{'label': 'disgust', 'score': 0.7899409532546997},
{'label': 'joy', 'score': 0.9664386510848999},
{'label': 'optimism', 'score': 0.6123248934745789}]
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}
}