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---
license: apache-2.0
language:
- orm
datasets:
- cis-lmu/Glot500
- castorini/afriberta-corpus
- statmt/cc100
- legacy-datasets/wikipedia
- csebuetnlp/xlsum
- allenai/nllb
- allenai/MADLAD-400
library_name: transformers
pipeline_tag: text-generation
tags:
- goldfish
- arxiv:2408.10441
---
# orm_latn_5mb
Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the <b>Oromo</b> (Latin script) model trained on 5MB of data, after accounting for an estimated byte premium of 1.26; content-matched text in Oromo takes on average 1.26x 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: orm_latn is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. Individual language code gaz_latn (West Central Oromo) is included in Goldfish, although with less data.
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: 39087104
* Maximum sequence length: 512 tokens
* Training text data (raw): 6.32MB
* Training text data (byte premium scaled): 5.005MB
* Training tokens: 1502208 (x10 epochs)
* Vocabulary size: 50000
* Compute cost: 1135923733463040.0 FLOPs or ~0.1 NVIDIA A6000 GPU hours
Training datasets (percentages prior to deduplication):
* 32.67081%: [Glot500](https://huggingface.co/datasets/cis-lmu/Glot500), including [AfriBERTa](https://huggingface.co/datasets/castorini/afriberta-corpus), [AfroMAFT](https://zenodo.org/record/6990611#.Y0-yU-xBw-Q), [CC100](https://huggingface.co/datasets/statmt/cc100), [CCNet](https://github.com/facebookresearch/cc_net), [HornMT](https://github.com/asmelashteka/HornMT), [Wortschatz Leipzig Data](https://wortschatz.uni-leipzig.de/en/download), [MoT](https://github.com/bltlab/mot), [Parallel Corpora for Ethiopian Languages](https://github.com/AAUThematic4LT/Parallel-Corpora-for-Ethiopian-Languages), [TICO](https://tico-19.github.io/), [Wikipedia Hugging Face](https://huggingface.co/datasets/legacy-datasets/wikipedia), [XLSum](https://huggingface.co/datasets/csebuetnlp/xlsum)
* 27.67571%: [NLLB (CommonCrawl and ParaCrawl)](https://huggingface.co/datasets/allenai/nllb)
* 23.09955%: [MADLAD-400 (CommonCrawl)](https://huggingface.co/datasets/allenai/MADLAD-400)
* 14.69511%: [AfriBERTa](https://huggingface.co/datasets/castorini/afriberta-corpus)
* 1.12785%: [eBible](https://ebible.org/find/)
* 0.73097%: [Wikipedia 2023/08](https://dumps.wikimedia.org/)
## 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},
}
```
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