File size: 1,713 Bytes
dd854fb 05bf00d dd854fb 8469e74 2a9e2b0 ccf8a46 2a9e2b0 ccf8a46 2a9e2b0 fb09215 2a9e2b0 ccf8a46 2a9e2b0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
---
language:
- en
license: mit
datasets:
- cardiffnlp/super_tweeteval
pipeline_tag: text-classification
---
# 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")
pipe(text)
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}
}
``` |