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--- |
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license: apache-2.0 |
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language: |
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- mai |
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datasets: |
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- allenai/nllb |
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- allenai/MADLAD-400 |
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- cis-lmu/Glot500 |
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- sil-ai/bloom-lm |
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- legacy-datasets/wikipedia |
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- oscar-corpus/OSCAR-2109 |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- goldfish |
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- arxiv:2408.10441 |
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--- |
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# mai_deva_full |
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Goldfish is a suite of monolingual language models trained for 350 languages. |
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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. |
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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). |
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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). |
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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). |
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Training code and sample usage: https://github.com/tylerachang/goldfish |
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Sample usage also in this Google Colab: [link](https://colab.research.google.com/drive/1rHFpnQsyXJ32ONwCosWZ7frjOYjbGCXG?usp=sharing) |
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## Model details: |
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To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/blob/main/model_details.json. |
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All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences. |
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For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)! |
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Details for this model specifically: |
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* Architecture: gpt2 |
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* Parameters: 124770816 |
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* Maximum sequence length: 512 tokens |
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* Training text data (raw): 99.73MB |
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* Training text data (byte premium scaled): 41.735MB |
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* Training tokens: 11159040 (x10 epochs) |
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* Vocabulary size: 50000 |
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* Compute cost: 5.6935272480768e+16 FLOPs or ~5.4 NVIDIA A6000 GPU hours |
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Training datasets (percentages prior to deduplication): |
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* 67.00767%: [NLLB (CommonCrawl and ParaCrawl)](https://huggingface.co/datasets/allenai/nllb) |
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* 12.01673%: [MADLAD-400 (CommonCrawl)](https://huggingface.co/datasets/allenai/MADLAD-400) |
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* 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) |
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* 8.75652%: [Wikipedia 2023/08](https://dumps.wikimedia.org/) |
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* 1.83462%: [eBible](https://ebible.org/find/) |
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* 0.01578%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) |
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## Citation |
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If you use this model, please cite: |
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``` |
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@article{chang-etal-2024-goldfish, |
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title={Goldfish: Monolingual Language Models for 350 Languages}, |
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author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.}, |
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journal={Preprint}, |
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year={2024}, |
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url={https://www.arxiv.org/abs/2408.10441}, |
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} |
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``` |
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