metadata

license: apache-2.0
language:
  - kaa
datasets:
  - allenai/MADLAD-400
  - cis-lmu/Glot500
  - legacy-datasets/wikipedia
library_name: transformers
pipeline_tag: text-generation
tags:
  - goldfish

kaa_latn_5mb

Goldfish is a suite of monolingual language models trained for 350 languages. This model is the Kara-Kalpak (Latin script) model trained on 5MB of data, after accounting for an estimated byte premium of 1.23; content-matched text in Kara-Kalpak takes on average 1.23x 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: kaa_latn is an individual language code. It is not contained in any macrolanguage codes contained in Goldfish (for script latn).

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/model_details.json. All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences. Details for this model specifically:

Architecture: gpt2
Parameters: 39087104
Maximum sequence length: 512 tokens
Training text data (raw): 6.14MB
Training text data (byte premium scaled): 5.005MB
Training tokens: 1245184 (x10 epochs)
Vocabulary size: 50000
Compute cost: 942148052582400.0 FLOPs or ~0.1 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):

97.52670%: MADLAD-400 (CommonCrawl)
1.65677%: Wikipedia 2023/08
0.81649%: Glot500, including Tatoeba, Wikipedia Hugging Face
0.00004%: Tatoeba

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},
}