---
language:
- az
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- text-retrieval
dataset_info:
  features:
  - name: text
    dtype: string
  - name: id
    dtype: string
  splits:
  - name: train
    num_bytes: 385090113.5570401
    num_examples: 50989
  download_size: 150035214
  dataset_size: 385090113.5570401
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- legal
---
# Azerbaijani Law Corpus

This dataset contains laws, acts, and other legal documents of the Republic of Azerbaijan. All of the data has been collected from [e-qanun.az](e-qanun.az).

Samples that contained less than 250 characters after preprocessing were left out, so you may not find some small documents.

If you are looking for a specific law, find its id on [e-qanun.az](https://e-qanun.az/), and then use this id to find the text. For example, id of the Labor Code is 46943: [https://e-qanun.az/framework/46943](https://e-qanun.az/framework/46943)

The following documents are not available: `[29445]`  
  
We have not scraped documents beyond `55103`.

If you use this dataset, please cite us:
```bib
@inproceedings{isbarov-etal-2024-open,
    title = "Open foundation models for {A}zerbaijani language",
    author = "Isbarov, Jafar  and
      Huseynova, Kavsar  and
      Mammadov, Elvin  and
      Hajili, Mammad and
      Ataman, Duygu",
    editor = {Ataman, Duygu  and
      Derin, Mehmet Oguz  and
      Ivanova, Sardana  and
      K{\"o}ksal, Abdullatif  and
      S{\"a}lev{\"a}, Jonne  and
      Zeyrek, Deniz},
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Turkic Languages (SIGTURK 2024)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.sigturk-1.2",
    pages = "18--28",
    abstract = "The emergence of multilingual large language models has enabled the development of language understanding and generation systems in Azerbaijani. However, most of the production-grade systems rely on cloud solutions, such as GPT-4. While there have been several attempts to develop open foundation models for Azerbaijani, these works have not found their way into common use due to a lack of systemic benchmarking. This paper encompasses several lines of work that promote open-source foundation models for Azerbaijani. We introduce (1) a large text corpus for Azerbaijani, (2) a family of encoder-only language models trained on this dataset, (3) labeled datasets for evaluating these models, and (4) extensive evaluation that covers all major open-source models with Azerbaijani support.",
}
```