Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the
Dari
(Arabic script) model trained on 5MB of data, after accounting for an estimated byte premium of 1.66; content-matched text in Dari takes on average 1.66x 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: prs_arab is an
individual language
code. Macrolanguage code fas_arab (Persian) is included in Goldfish. Consider using that model depending on your use case.
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: 25455616
Maximum sequence length: 512 tokens
Training text data (raw): 8.32MB
Training text data (byte premium scaled): 5.005MB
Training tokens: 1207808 (x10 epochs)
Vocabulary size: 23971
Compute cost: 913441979105280.0 FLOPs or ~0.1 NVIDIA A6000 GPU hours
Training datasets (percentages prior to deduplication):
@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},
}
Runs of goldfish-models prs_arab_5mb on huggingface.co
94
Total runs
0
24-hour runs
30
3-day runs
39
7-day runs
82
30-day runs
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