For training a model to classify social media comments as toxic and non-toxic, you would use the following types of data:

1. Labeled Comment Datasets: Use a dataset with comments or posts already labeled as “toxic” or “non-toxic.” Examples include the Jigsaw Toxic Comment Classification Challenge dataset or other publicly available datasets from platforms like Kaggle.
2. Feature Data: Extract features from the comments, such as:
   • Textual content: The actual words and phrases used.
   • Metadata: Information like the time of the comment, user profile details, or context.
3. Pre-Processed Text: Include cleaned and pre-processed versions of the text, with steps like tokenization, lowercasing, removing stop words, and stemming or lemmatization.
4. Contextual Data: Sometimes additional context, like previous comments or user history, can help improve model accuracy.
5. Diverse Samples: Ensure the dataset is diverse and representative of different languages, slang, and comment types to build a robust model.

Using a combination of these data types helps create a well-rounded model capable of accurately classifying comments in various contexts.