metadata
license: mit
datasets:
- badmatr11x/hate-offensive-speech
language:
- en
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: text-classification
tags:
- code
widget:
- text: People are fun to talk.
example_title: Neither Speech
- text: Black people are good at running.
example_title: Hate Speech
- text: And I'm goin back to school, only for the hoes and a class or two.
example_title: Offensive Speech
This is the Offensive and Hateful Speech Detection mode fine-tuned on the distilroberta-base model available on the huggingface pre-trained models. This model is trained with the dataset which contains around 55K annotated tweets; classified into three different categories, Hateful, Offensive and Neither.
This is the example of the dataset instance:
{
"label": {
0: "Hate Speech",
1: "Offensive Speech",
2: "Neither"
}
"tweet": <string>
}
Model is fine-tuned on epochs number 5 with over than 15500 rounds of training. The self-verified evaluation accuracy of the models is 95.60% with the evaluation lost 17.02%. The testing accuracy of the model is recored 95.04%, self stated.