|
|
| --- |
| license: apache-2.0 |
| language: |
| - mai |
| datasets: |
| - allenai/nllb |
| - allenai/MADLAD-400 |
| - cis-lmu/Glot500 |
| - sil-ai/bloom-lm |
| - legacy-datasets/wikipedia |
| - oscar-corpus/OSCAR-2109 |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: |
| - goldfish |
| - arxiv:2408.10441 |
| --- |
| |
| # mai_deva_full |
|
|
| Goldfish is a suite of monolingual language models trained for 350 languages. |
| This model is the <b>Maithili</b> (Devanagari script) model trained on 41MB of data (all our data in the language), after accounting for an estimated byte premium of 2.39; content-matched text in Maithili takes on average 2.39x as many UTF-8 bytes to encode as English. |
| The Goldfish models are trained primarily for comparability across languages and for low-resource languages; Goldfish performance for high-resource languages is not designed to be comparable with modern large language models (LLMs). |
|
|
| Note: mai_deva is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. It is not contained in any macrolanguage codes contained in Goldfish (for script deva). |
| |
| All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://www.arxiv.org/abs/2408.10441). |
| |
| Training code and sample usage: https://github.com/tylerachang/goldfish |
| |
| Sample usage also in this Google Colab: [link](https://colab.research.google.com/drive/1rHFpnQsyXJ32ONwCosWZ7frjOYjbGCXG?usp=sharing) |
| |
| ## Model details: |
| |
| To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/blob/main/model_details.json. |
| All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences. |
| For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)! |
| Details for this model specifically: |
|
|
| * Architecture: gpt2 |
| * Parameters: 124770816 |
| * Maximum sequence length: 512 tokens |
| * Training text data (raw): 99.73MB |
| * Training text data (byte premium scaled): 41.735MB |
| * Training tokens: 11159040 (x10 epochs) |
| * Vocabulary size: 50000 |
| * Compute cost: 5.6935272480768e+16 FLOPs or ~5.4 NVIDIA A6000 GPU hours |
|
|
| Training datasets (percentages prior to deduplication): |
| * 67.00767%: [NLLB (CommonCrawl and ParaCrawl)](https://huggingface.co/datasets/allenai/nllb) |
| * 12.01673%: [MADLAD-400 (CommonCrawl)](https://huggingface.co/datasets/allenai/MADLAD-400) |
| * 10.36867%: [Glot500](https://huggingface.co/datasets/cis-lmu/Glot500), including [BLOOM](https://huggingface.co/datasets/sil-ai/bloom-lm), [Wortschatz Leipzig Data](https://wortschatz.uni-leipzig.de/en/download), [OSCAR](https://oscar-project.org/), [Wikipedia Hugging Face](https://huggingface.co/datasets/legacy-datasets/wikipedia) |
| * 8.75652%: [Wikipedia 2023/08](https://dumps.wikimedia.org/) |
| * 1.83462%: [eBible](https://ebible.org/find/) |
| * 0.01578%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) |
|
|
|
|
| ## Citation |
|
|
| If you use this model, please cite: |
|
|
| ``` |
| @article{chang-etal-2024-goldfish, |
| title={Goldfish: Monolingual Language Models for 350 Languages}, |
| author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.}, |
| journal={Preprint}, |
| year={2024}, |
| url={https://www.arxiv.org/abs/2408.10441}, |
| } |
| ``` |
|
|
