Classified helps you classify LLM pretraining or instruction data by quality. It can be used to summarize dataset quality, filter a dataset, or train custom classifiers (coming soon). It works with datasets on disk or streamed from the Huggingface Hub.

You can classify according to multiple lenses (see the app/lenses folder for examples). Each lens rates the data in a different way, according to your needs. Currently, it supports these lenses:

Pretraining (single field)

• code_quality - quality of code pretraining data
• textbook_quality - quality of textbook-like data
• learning_value - learning value of textbook-like data (similar to phi)
• textbook_depth - depth/specificity of textbook-like data

Instruct (instruction/output pairs)

• instruction_following - how well outputs follow instructions

By default, GPT-4 is used to do the rating. You can also use GPT-3.5-turbo, which is faster but less accurate. Soon, you'll also be able to train custom models to do the rating.

You'll need Python 3.9+ (ideally 3.11).

• git clone https://github.com/VikParuchuri/classified.git
• cd classified
• poetry install
• alembic upgrade head - this will make a sqlite database

You can override any setting in app/settings.py by setting an environment variable with the same name, or creating a local.env file with the key.

• You will need to set your OPENAI_KEY.
• You can also configure CHAT_MODEL - this will change the openai model used for rating. It is gpt-4 by default. You can change it to gpt-3.5-turbo also. GPT-4 gives the best results.

This will give you an overview of how good a pretraining dataset is. You can rate any dataset on the huggingface hub, or a local dataset file. It will print some stats to the console, dump them to a csv file, and show the full scoring and rationale in jsonl files inside app/data.

Usage example:

python summary.py vikp/textbooks_grounded markdown textbook_quality --max 25 --workers 2 --stream --version 1

Raw Scores

Lens                overall
----------------  ---------
textbook_quality        2.8

Summary
Lens              Poor    Low    Medium    Good      Total
----------------  ------  -----  --------  ------  -------
textbook_quality  0.00%   0.00%  13.04%    86.96%       23

• You can use any dataset on huggingface hub, or a local dataset. Here we use vikp/textbook_quality_programming.
• Specify which column you want to rate (markdown in the example).
• Specify which rating lenses you want to run (textbook_quality in the example). Comma separate multiple lenses to run them at once.
• --max specifies the maximum number of examples you want to rate. Data will be shuffled first.
• --workers specifies the number of parallel workers to use. This will be limited by your OpenAI rate limit. With the default GPT-4 rate limit, you will want to set this to 2.
• --stream will stream the dataset from the huggingface hub instead of downloading it all.

Check the help for additional cli options.

You can also do this for instruct data. Usage example:

python summary.py vikp/code_instructions_filtered instruction,output instruction_following --max 25 --workers 2 --stream

The main difference from pretraining data is that two fields are specified (instruction,output). This is because the instruction_following lens requires two inputs, the instruction and the associated output.

All data is cached by default. The cache is specific to the lens type, input data, and model. Use the --version flag if you want to bypass the cache and re-rate, for example if you want to see if the ratings change.

You can add new lenses in the app/lenses folder. You will need to create system.jinja for the system prompt, prompt.jinja for the main prompt, config.json for the lens config, and functions.json to store the function call. Look to the existing tasks for examples.

You can then use the lenses in any of the scripts above.

The functionality to train custom models and use them to filter larger datasets is coming soon.