Reviewing the last image only.

Loading data from Talking to Chatbots Dataset reddgr/talking-to-chatbots-chats

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the code editor in visual editor
🤖 Screen capture of a Visual Studio Code window showing a Python script in a Jupyter notebook environment. The left sidebar displays an explorer view with several folders and files, including “MIAX_practicas”, “python_scripts”, and specific files like “LICENSE” and “README.md”. The main window shows a Python script titled `datasets.ipynb`, which downloads a dataset. The script includes code for loading the dataset and displaying sample data. A table at the bottom of the screen contains sample conversation data, displaying fields like “conversation_id”, “model”, “conversation”, “turn”, “language”, “openai_moderation”, and “redacted”. Two example conversations are shown, one in English and another in French.

[Alt text by ALT Text Artist GPT]
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the code editor in visual editor
🤖 Screen capture of a Visual Studio Code window featuring a Python script within a Jupyter notebook environment. On the left, the sidebar shows file directories including “MIAX_practicas”, “python_scripts”, and files like “LICENSE” and “README.md”. The main area shows a script titled `datasets.ipynb`, which is designed to load and explore a dataset. The displayed code includes loading a dataset, printing its structure, and displaying a sample. Below the code block, the output displays sample data from the dataset in a table format, containing columns like “conversation_id”, “model”, “conversation”, “turn”, “language”, “openai_moderation”, and “redacted”. Two rows of data are visible: one conversation in English and another in French.

[Alt text by ALT Text Artist GPT]
😎 NO, YOU DIDN’T READ THE LAST IMAGE ONLY

the code editor in visual editor
🤖 Screen capture of a Visual Studio Code window with a Jupyter notebook open in the main area. The notebook is named `datasets.ipynb`, and the visible code block is for loading and exploring a dataset. The script loads a dataset from the “lmsys/lmsys-chat-1m” repository and prints the dataset’s structure. The dataset contains fields like “conversation_id”, “model”, “conversation”, “turn”, “language”, “openai_moderation”, and “redacted”. Below the code, the output shows a table of sample data with three columns visible: “conversation_id”, “model”, and “conversation”. Two sample rows of data are displayed, including conversations in English and French. The left sidebar lists directories and files, such as “MIAX_practicas”, “python_scripts”, and “LICENSE”.

[Alt text by ALT Text Artist GPT]

Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: