|
--- |
|
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](https://huggingface.co/datasets/badmatr11x/hate-offensive-speech/) 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. |
