---
model-index:
- name: twitter-roberta-base-hate-latest
  results: []
pipeline_tag: text-classification
---
# 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.

## Following metrics are achieved 
| **Dataset**                                                                                                                                          | **Accuracy** | **Macro-F1** | **Weighted-F1** |
|------------------------------------------------------------------------------------------------------------------------------------------------------|:------------:|:------------:|:---------------:|
| hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter                                          |    0.5848    |    0.5657    |      0.5514     |
| ucberkeley-dlab/measuring-hate-speech                                                                                                                |    0.8706    |    0.8531    |      0.8701     |
| Detecting East Asian Prejudice on Social Media                                                                                                       |    0.9276    |    0.8935    |      0.9273     |
| Call me sexist, but                                                                                                                                  |    0.9033    |    0.6288    |      0.8852     |
| Predicting the Type and Target of Offensive Posts in Social Media                                                                                    |    0.9075    |    0.5984    |      0.8935     |
| HateXplain                                                                                                                                           |    0.9594    |    0.8024    |      0.9600     |
| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior |    0.6817    |    0.5939    |      0.6233     |
| Twitter Sentiment Analysis                                                                                                                           |    0.9808    |    0.9258    |      0.9807     |
| Overview of the HASOC track at FIRE 2019:Hate Speech and Offensive Content Identification in Indo-European Languages                                 |    0.8665    |    0.5562    |      0.8343     |
| Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter                                                          |    0.9465    |    0.8557    |      0.9440     |
| Automated Hate Speech Detection and the Problem of Offensive Language                                                                                |    0.9116    |    0.8797    |      0.9100     |
| Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter                                                          |    0.8378    |    0.8338    |      0.8385     |
| Multilingual and Multi-Aspect Hate Speech Analysis                                                                                                   |    0.9655    |    0.4912    |      0.9824     |
| **Overall**                                                                                                                                          |  **0.8827**  |  **0.8383**  |    **0.8842**   |


### 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'}

```