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README.md

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----
-license: mit
-datasets:
-- badmatr11x/hate-offensive-speech
-language:
-- en
-metrics:
-- accuracy
-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 am 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.