| --- |
| 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 |
