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cardiffnlp/twitter-roberta-base-emoji-latest
twitter-roberta-base-emoji-latest is a text classification model from cardiffnlp. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 and finetuned for emoji classification (multiclass classification on 100 emojis) on the TweetEmoji100 dataset of SuperTweetEval. The o…
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.safetensors499 MB · 50%
From the Hugging Face model README
This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 and finetuned for emoji classification (multiclass classification on 100 emojis) on the TweetEmoji100 dataset of SuperTweetEval. The original Twitter-based RoBERTa model can be found here.
from transformers import pipeline
text= "I’m tired of being sick.. it’s been four days dawg"
pipe = pipeline('text-classification', model="cardiffnlp/twitter-roberta-base-emoji-latest", return_all_scores=True))
predictions = pipe(text)[0]
predictions = sorted(predictions, key=lambda d: d['score'], reverse=True)
predictions[:5]
>> [{'label': '😒', 'score': 0.14303581416606903},
{'label': '😩', 'score': 0.07775110006332397},
{'label': '😤', 'score': 0.0710175409913063},
{'label': '😑', 'score': 0.06665993481874466},
{'label': '😫', 'score': 0.0662984848022461}]
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}
}