Create README.md

README.md

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+---
+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.