metadata

license: apache-2.0
language:
  - aze
datasets:
  - cis-lmu/Glot500
  - allenai/c4
  - legacy-datasets/wikipedia
  - csebuetnlp/xlsum
  - allenai/nllb
  - oscar-corpus/OSCAR-2109
library_name: transformers
pipeline_tag: text-generation
tags:
  - goldfish

aze_arab_10mb

Goldfish is a suite of monolingual language models trained for 350 languages. This model is the Azerbaijani (Arabic script) model trained on 10MB of data, after accounting for an estimated byte premium of 1.20; content-matched text in Azerbaijani takes on average 1.20x 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: This language is available in Goldfish with other scripts (writing systems). See: aze_cyrl, aze_latn.

Note: aze_arab is a macrolanguage code. Individual language code azb_arab (South Azerbaijani) 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).

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): 11.99MB
Training text data (byte premium scaled): 10.005MB
Training tokens: 2203136 (x10 epochs)
Vocabulary size: 50000
Compute cost: 1666385628364800.0 FLOPs or ~0.2 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):

50.47341%: Glot500, including CCNet, Earthlings, Wortschatz Leipzig Data, MC4, TIL, W2C, Wikipedia Hugging Face, WikiMatrix, XLSum
22.81190%: NLLB (CommonCrawl and ParaCrawl)
21.00426%: Wikipedia 2023/08
5.71043%: OSCAR 2021/09

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},
}