Bot_anno.zip -

I’m making this dataset open-source to help the community build more empathetic and efficient bots. You can download the full archive [here] or find more technical setup guides on platforms like Hashnode .

Use the "unclear" or "other" tags in the annotation set to teach your bot when to ask for clarification.

Ground-truth labels help reduce "hallucinations" in LLMs. bot_anno.zip

Consider using AI-driven workflows to prep your data before final manual review. Get Involved

Using pre-annotated data like those found in Stack Overflow examples can save dozens of hours in the manual labeling phase. I’m making this dataset open-source to help the

This package is a curated collection of annotations specifically designed for [mention specific use case, e.g., intent classification or sentiment analysis]. Whether you are building a customer service bot or a creative AI assistant, these annotations provide the structured "truth" your model needs to learn effectively. Why Quality Annotations Matter

A unified annotation schema ensures your bot reacts predictably to similar user queries. How to Use the Dataset Ground-truth labels help reduce "hallucinations" in LLMs

import zipfile import os # Extracting the annotation files with zipfile.ZipFile('bot_anno.zip', 'r') as zip_ref: zip_ref.extractall('annotations_folder') print("Dataset ready for training!") Use code with caution. Tips for Better Bot Training