classified

KO | EN

gitlite — search

#javascript #python #hacktoberfest #typescript #go #golang #rust #deep-learning #docker #java #nodejs #kubernetes #pytorch #openai

(★ 54)

Score LLM pretraining data with classifiers

classified 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Classified

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.

## Install

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

## Configure

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.

# Summarize

## Pretraining

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.

## Instruct Data

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.

# Caching

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.

# Adding new lenses

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.

# Train custom models

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

classified

classified 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

classified

classified 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README