---
language:
- en
license: mit
datasets:
- cardiffnlp/super_tweeteval
pipeline_tag: text-classification
inference:
  parameters:
    return_all_scores: True
widget:
- text: >-
    I’m tired of being sick.. it’s been four days dawg
---
# cardiffnlp/twitter-roberta-base-emoji-latest

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](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m).

## Example
```python
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}]
```

## Citation Information
Please cite the [reference paper](https://arxiv.org/abs/2310.14757) if you use this model.

```bibtex
@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}
}
```