llm-datasets 0.0.3

A collection of datasets for language model training including scripts for downloading, preprocesssing, and sampling.

llm-datasets

llm-datasets is a collection of datasets for language model training including scripts for downloading, preprocesssing, and sampling.

The documentation is available here.

Quick start

Installation

Install the llm-datasets package with pip:

pip install llm-datasets

In order to keep the package minimal by default, llm-datasets comes with optional dependencies useful for some use cases. For example, if you want to have the text extraction for all available datasets, run:

pip install llm-datasets[datasets]

Download and text extraction

To download and extract the plain-text of one or more datasets, run the following command:

llm-datasets extract_text $DATASET_ID $OUTPUT_DIR

By default, output is saved as JSONL files. To change the output format, you can use the --output_format argument as below:

llm-datasets extract_text $DATASET_ID $OUTPUT_DIR --output_format parquet  --output_compression zstd

Available datasets

A list or table with all available datasets can be print with the follow command:

llm-datasets print_stats --print_output md

Token count by language

Language	Tokens
bg	31 B
ca	6 B
code	212 B
cs	42 B
da	13 B
de	160 B
el	63 B
en	1 T
es	101 B
et	9 B
eu	1 B
fi	19 B
fr	84 B
ga	274 M
gl	231 M
hr	11 B
hu	52 B
it	61 B
lt	7 B
lv	5 B
mt	4 B
nl	44 B
nn	76 M
no	13 B
pl	45 B
pt	46 B
ro	18 B
sh	184 M
sk	32 B
sl	13 B
sr	11 B
sv	19 B
uk	56 B

Token count by source

Source	Tokens
curlicat	963 M
macocu	74 B
redpajama	44 B
wura	N/A
wikihow	99 M
pes2o	57 B
proof_pile	12 B
pile_of_law	111 B
math_amps	7 B
edgarcorpus	N/A
bulgarian_news	640 M
bulnc	4 B
openlegaldata	7 B
dewac	3 B
ga_bilingual_legistation	4 k
ga_universal_dependencies	40 k
hrwac	2 B
styria_news	432 M
croatian_news_engri	1 B
itwac	3 B
korpus_malti	816 M
sonar	746 M
cc_gigafida	260 M
academic_slovene_kas	3 B
slwac_web	3 B
sk_court_decisions	24 B
sk_laws	105 M
syn_v9	13 B
cs_en_parallel	473 M
danish_gigaword	2 B
danewsroom	835 M
dk_clarin	80 M
cabernet	599 M
norwegian_cc	11 B
pl_nkjp	3 M
pl_parliamentary_corpus	1 B
parlamento_pt	732 M
brwac	4 B
seimas_lt_en	12 k
state_related_latvian_web	52 k
greek_legal_code	80 M
greek_web_corpus	11 B
estonian_reference_corpus	481 M
enc2021	3 B
ekspress	723 M
euscrawl	831 M
spanish_legal	1 B
ylenews	286 M
sv_gigaword	528 M
srpkor	866 M
marcell_legislative_subcorpus_v2	1 B
uk_laws	2 B
eurlex	41 B
legal_mc4	28 B
wiki	21 B
wikibooks	313 M
wikiquote	247 M
wikinews	90 M
wikisource	2 B
wikivoyage	119 M
colossal_oscar	2 T
starcoder	212 B

Dataset viewer

We provide a Web-based application through streamlit to browse all datasets and their contained text content. To start the app, first clone this repository, install dependencies, and run the following command:

# clone is needed since streamlit does not support apps from modules yet
git clone https://github.com/malteos/llm-datasets.git

streamlit run src/lm_datasets/viewer/app.py -- \
    --raw_datasets_dir=$RAW_DATASETS_DIR \
    --output_dir=$PROCESSED_DATASET_DIR

Development & Contributions

Setup environment

To setup, your local development environment we recommend conda and cloning the repository. The repository also includes settings and launch scripts for VSCode.

git clone [e-mail]:malteos/llm-datasets.git
cd llm-datasets

conda create -n llm-datasets python=3.10
conda activate llm-datasets

pip install -r requirements.txt

Alternatively, you can install the Python package directly from the dev branch:

pip install git+https://github.com/malteos/llm-datasets.git@dev

Install the pre-commit hooks

This repository uses git hooks to validate code quality and formatting.

pre-commit install
git config --bool flake8.strict true  # Makes the commit fail if flake8 reports an error

To run the hooks:

pre-commit run --all-files

Testing

The tests can be executed with:

pytest --doctest-modules --cov-report term --cov=llm_datasets

Acknowledgements

The work on the llm-datasets software is partially funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the project OpenGPT-X (project no. 68GX21007D).

License

Apache 2.0

(Please note that the actual datasets are released with different licenses)