Update README.md

README.md

@@ -28,3 +28,47 @@ configs:
   - split: test
     path: data/test-*
 ---
+# Combined Dataset
+
+This dataset contains tweets classified into various categories with an additional moderator label to indicate safety.
+
+## Features
+
+- **tweet**: The text of the tweet.
+- **class**: The category of the tweet (e.g., `neutral`, `hatespeech`, `counterspeech`).
+- **data**: Additional information about the tweet.
+- **moderator**: A label indicating if the tweet is `safe` or `unsafe`.
+
+## Usage
+
+This dataset is intended for training models in text classification, hate speech detection, or sentiment analysis.
+
+## Licensing
+
+This dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
+
+
+Hatebase data set has been curated from multiple benchmark datasets and converted into binary class problem.
+These are the following benchmark dataset:
+HateXplain : Converted hate,offensive, neither into binary Classification
+Peace Violence :Converted  Peace and Violence, 4 classes into binary Classification 
+Hate Offensive : Converted hate,offensive, neither into binary Classification
+OWS
+Go Emotion
+CallmeSexistBut.. : Binary classification along with toxicity score 
+Slur : Based on slur, multiclass problem (DEG,NDEG,HOM, APPR)
+Stormfront : Whitesupermacist forum with Binary Classification
+UCberkley_HS : Multilclass hatespeech, counter hs or neutral (It has continuous score for eac class which is converted in our case)
+BIC (Each of 3 class has categorical score which is converted into binary using a threshold of 0.5) offensive, intent and lewd (sexual) -->
+
+
+train example: 222196
+test examples: 24689
+
+## Example
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("your-hf-username/combined-dataset")
+print(dataset['train'][0])