metadata

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.

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, and then use this id to find the text. For example, id of the Labor Code is 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:

@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.",
}