Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the
Northern Uzbek
(Latin script) model trained on 145MB of data (all our data in the language), after accounting for an estimated byte premium of 1.65; content-matched text in Northern Uzbek takes on average 1.65x 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: uzn_latn is an
individual language
code. Macrolanguage code uzb_latn (Uzbek) 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: 124770816
Maximum sequence length: 512 tokens
Training text data (raw): 239.61MB
Training text data (byte premium scaled): 145.595MB
Training tokens: 52049408 (x10 epochs)
Vocabulary size: 50000
Compute cost: 2.65611450580992e+17 FLOPs or ~25.1 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 uzn_latn_full on huggingface.co
19
Total runs
0
24-hour runs
1
3-day runs
2
7-day runs
-11
30-day runs
More Information About uzn_latn_full huggingface.co Model
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