Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the
Amharic
(Ge'ez script) model trained on 5MB of data, after accounting for an estimated byte premium of 1.72; content-matched text in Amharic takes on average 1.72x 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: amh_ethi is an
individual language
code. It is not contained in any macrolanguage codes contained in Goldfish (for script ethi).
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): 8.61MB
Training text data (byte premium scaled): 5.005MB
Training tokens: 1136128 (x10 epochs)
Vocabulary size: 50000
Compute cost: 859206482657280.0 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 amh_ethi_5mb on huggingface.co
32
Total runs
0
24-hour runs
1
3-day runs
9
7-day runs
23
30-day runs
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