File size: 1,676 Bytes
f6a1aff f20e282 f6a1aff 27de190 f6a1aff 27de190 f6a1aff |
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 42 43 44 45 46 47 48 49 50 |
---
language:
- en
license: mit
datasets:
- cardiffnlp/super_tweeteval
pipeline_tag: text-classification
---
# cardiffnlp/twitter-roberta-large-hate-latest
This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 and finetuned for hate speech detection (multiclass classification) on the _TweetHate_ 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-large-2022-154m).
# Labels
<code>
"id2label": {
"0": "hate_gender",
"1": "hate_race",
"2": "hate_sexuality",
"3": "hate_religion",
"4": "hate_origin",
"5": "hate_disability",
"6": "hate_age",
"7": "not_hate"
}
</code>
## Example
```python
from transformers import pipeline
text = 'Eid Mubarak Everyone!!! ❤ May Allah unite all Muslims, show us the right path, and bless us with good health.❣'
pipe = pipeline('text-classification', model="cardiffnlp/twitter-roberta-large-hate-latest")
pipe(text)
>> [{'label': 'not_hate', 'score': 0.9997966885566711}]
```
## 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}
}
``` |