---
language:
- en
license: mit
datasets:
- cardiffnlp/super_tweeteval
pipeline_tag: text-classification
---
# 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](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).

## Labels
<code>
    "id2label": {
    "0": "anger",
    "1": "anticipation",
    "2": "disgust",
    "3": "fear",
    "4": "joy",
    "5": "love",
    "6": "optimism",
    "7": "pessimism",
    "8": "sadness",
    "9": "surprise",
    "10": "trust"
  }
</code>

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