ayr_latn_10mb

Goldfish is a suite of monolingual language models trained for 350 languages. This model is the Central Aymara (Latin script) model trained on 10MB of data, after accounting for an estimated byte premium of 1.10; content-matched text in Central Aymara takes on average 1.10x 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: ayr_latn is an individual language code. Macrolanguage code aym_latn (Aymara) is included in Goldfish. Consider using that model depending on your use case.

All training and hyperparameter details are in our paper, Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024).

Training code and sample usage: https://github.com/tylerachang/goldfish

Sample usage also in this Google Colab: link

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): 10.98MB
• Training text data (byte premium scaled): 10.005MB
• Training tokens: 2749440 (x10 epochs)
• Vocabulary size: 50000
• Compute cost: 2078071786045440.0 FLOPs or ~0.2 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):

• 100.00000%: NLLB (CommonCrawl and ParaCrawl) and NLLB Multi-Domain

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},
}