File size: 3,069 Bytes
47253b4 62f8f7e 3d440b6 47253b4 355a744 62f8f7e c74b053 62f8f7e 3d440b6 62f8f7e 3d440b6 685ed68 3d440b6 685ed68 22dfd1a 3d440b6 685ed68 62f8f7e |
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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
---
model-index:
- name: twitter-roberta-base-hate-latest
results: []
pipeline_tag: text-classification
language:
- en
---
# cardiffnlp/twitter-roberta-base-hate-latest
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for binary hate-speech classification.
A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model.
More details in the [reference paper](https://aclanthology.org/2023.woah-1.25/).
| **Dataset** | **Accuracy** | **Macro-F1** | **Weighted-F1** |
|:----------|-----------:|-----------:|--------------:|
| hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter | 0.5831 | 0.5646 | 0.548 |
| ucberkeley-dlab/measuring-hate-speech | 0.9273 | 0.9193 | 0.928 |
| Detecting East Asian Prejudice on Social Media | 0.9231 | 0.6623 | 0.9428 |
| Call me sexist, but | 0.9686 | 0.9203 | 0.9696 |
| Predicting the Type and Target of Offensive Posts in Social Media | 0.9164 | 0.6847 | 0.9098 |
| HateXplain | 0.8653 | 0.845 | 0.8662 |
| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior | 0.7801 | 0.7446 | 0.7614 |
| Multilingual and Multi-Aspect Hate Speech Analysis | 0.9944 | 0.4986 | 0.9972 |
| Hate speech and offensive content identification in indo-european languages | 0.8779 | 0.6904 | 0.8706 |
| Are You a Racist or Am I Seeing Things? | 0.921 | 0.8935 | 0.9216 |
| Automated Hate Speech Detection | 0.9423 | 0.9249 | 0.9429 |
| Hate Towards the Political Opponent | 0.8783 | 0.6595 | 0.8788 |
| Hateful Symbols or Hateful People? | 0.8187 | 0.7833 | 0.8323 |
| **Overall** | **0.8766** | **0.7531** | **0.8745** |
### Usage
Install tweetnlp via pip.
```shell
pip install tweetnlp
```
Load the model in python.
```python
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest")
model.predict('I love everybody :)')
>> {'label': 'NOT-HATE'}
```
### Reference paper - Model based on:
```
@inproceedings{antypas-camacho-collados-2023-robust,
title = "Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation",
author = "Antypas, Dimosthenis and
Camacho-Collados, Jose",
booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.woah-1.25",
pages = "231--242"
}
``` |