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--- |
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license: apache-2.0 |
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language: |
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- bjn |
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- msa |
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- may |
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datasets: |
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- allenai/nllb |
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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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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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# bjn_latn_10mb |
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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>Banjar</b> (Latin script) model trained on 10MB of data, after accounting for an estimated byte premium of 1.17; content-matched text in Banjar takes on average 1.17x 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: bjn_latn is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. Macrolanguage code msa_latn (Malay) is included in Goldfish. Consider using that model depending on your use case. |
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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: 39087104 |
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* Maximum sequence length: 512 tokens |
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* Training text data (raw): 11.67MB |
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* Training text data (byte premium scaled): 10.005MB |
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* Training tokens: 2742784 (x10 epochs) |
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* Vocabulary size: 50000 |
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* Compute cost: 2074159082373120.0 FLOPs or ~0.2 NVIDIA A6000 GPU hours |
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Training datasets (percentages prior to deduplication): |
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* 90.26394%: [NLLB (CommonCrawl and ParaCrawl)](https://huggingface.co/datasets/allenai/nllb) |
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* 5.99946%: [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), [NLLB_seed](https://github.com/facebookresearch/flores/blob/main/nllb_seed/README.md), [Wikipedia Hugging Face](https://huggingface.co/datasets/legacy-datasets/wikipedia) |
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* 3.53524%: [Wikipedia 2023/08](https://dumps.wikimedia.org/) |
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* 0.12237%: [IndoNLP](https://huggingface.co/indonlp) |
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* 0.07900%: [eBible](https://ebible.org/find/) |
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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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