metadata

license: apache-2.0
language:
  - ara
  - apc
datasets:
  - cis-lmu/Glot500
library_name: transformers
pipeline_tag: text-generation
tags:
  - goldfish

apc_arab_5mb

Goldfish is a suite of monolingual language models trained for 350 languages. This model is the Levantine Arabic (Arabic script) model trained on 5MB of data, after accounting for an estimated byte premium of 1.47; content-matched text in Levantine Arabic takes on average 1.47x 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: apc_arab is an individual language code. However, you may also want to consider the arb_arab (Standard Arabic) models which are trained on more data.

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. Details for this model specifically:

Architecture: gpt2
Parameters: 39087104
Maximum sequence length: 512 tokens
Training text data (raw): 7.37MB
Training text data (byte premium scaled): 5.005MB
Training tokens: 1219072 (x10 epochs)
Vocabulary size: 50000
Compute cost: 921267386449920.0 FLOPs or ~0.1 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):

99.99983%: Glot500, including ADD, AraBench, DART, Habibi, QADI, Shami, Tatoeba
0.00017%: 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},
}