--- license: cc-by-nc-sa-4.0 dataset_info: - config_name: anl-news features: - name: text dtype: string splits: - name: train num_bytes: 1500707584 num_examples: 236443 download_size: 773593491 dataset_size: 1500707584 - config_name: azwiki features: - name: id dtype: int64 - name: text dtype: string - name: title dtype: string splits: - name: train num_bytes: 360206818 num_examples: 129433 download_size: 204669909 dataset_size: 360206818 - config_name: bhos features: - name: title dtype: string - name: text dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 736156688 num_examples: 488390 download_size: 417517945 dataset_size: 736156688 - config_name: elite-blogs features: - name: id dtype: int64 - name: source dtype: string - name: text dtype: string splits: - name: train num_bytes: 7625261 num_examples: 755 download_size: 4031201 dataset_size: 7625261 - config_name: elite-books features: - name: text dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 38894982 num_examples: 104 download_size: 22016093 dataset_size: 38894982 - config_name: eqanun features: - name: text dtype: string - name: title dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 404638424 num_examples: 53656 download_size: 149151917 dataset_size: 404638424 - config_name: mediocore-books features: - name: ID dtype: string - name: ' Metadata' dtype: string - name: text dtype: string splits: - name: train num_bytes: 2908183660 num_examples: 7807263 download_size: 695603782 dataset_size: 2908183660 - config_name: translated-enwiki features: - name: text dtype: string splits: - name: train num_bytes: 1629190007 num_examples: 280465 download_size: 919526548 dataset_size: 1629190007 configs: - config_name: anl-news data_files: - split: train path: anl-news/train-* - config_name: azwiki data_files: - split: train path: azwiki/train-* - config_name: bhos data_files: - split: train path: bhos/train-* - config_name: elite-blogs data_files: - split: train path: elite-blogs/train-* - config_name: elite-books data_files: - split: train path: elite-books/train-* - config_name: eqanun data_files: - split: train path: eqanun/train-* - config_name: mediocore-books data_files: - split: train path: mediocore-books/train-* - config_name: translated-enwiki data_files: - split: train path: translated-enwiki/train-* task_categories: - fill-mask language: - az size_categories: - 1M<n<10M --- 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.", } ``` https://arxiv.org/abs/2407.02337