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udm_cyrl_full

Text Generation
Transformers
PyTorch
Udmurt
gpt2
goldfish
text-generation-inference
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udm_cyrl_full
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---
license: apache-2.0
language:
- udm
datasets:
- allenai/MADLAD-400
- cis-lmu/Glot500
- legacy-datasets/wikipedia
library_name: transformers
pipeline_tag: text-generation
tags:
- goldfish

---

# udm_cyrl_full

Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the <b>Udmurt</b> (Cyrillic script) model trained on 51MB of data (all our data in the language), after accounting for an estimated byte premium of 1.74; content-matched text in Udmurt takes on average 1.74x 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: udm_cyrl is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. It is not contained in any macrolanguage codes contained in Goldfish (for script cyrl).

All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://github.com/tylerachang/goldfish/blob/main/goldfish_paper_20240815.pdf).

Training code and sample usage: https://github.com/tylerachang/goldfish

Sample usage also in this Google Colab: [link](https://colab.research.google.com/drive/1rHFpnQsyXJ32ONwCosWZ7frjOYjbGCXG?usp=sharing)

## Model details:

To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/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): 90.10MB
* Training text data (byte premium scaled): 51.775MB
* Training tokens: 10932736 (x10 epochs)
* Vocabulary size: 50000
* Compute cost: 5.577670361088e+16 FLOPs or ~5.3 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):
* 80.17663%: [MADLAD-400 (CommonCrawl)](https://huggingface.co/datasets/allenai/MADLAD-400)
* 10.77909%: [Languages of Russia](http://web-corpora.net/wsgi3/minorlangs/download)
* 4.96507%: [Glot500](https://huggingface.co/datasets/cis-lmu/Glot500), including [Earthlings](https://publicdata.canterbury.ac.nz/Research/Geocorpus/CCGLU_v5.0/), [Wortschatz Leipzig Data](https://wortschatz.uni-leipzig.de/en/download), [Tatoeba](https://tatoeba.org/en/), [Wikipedia Hugging Face](https://huggingface.co/datasets/legacy-datasets/wikipedia)
* 4.07691%: [Wikipedia 2023/08](https://dumps.wikimedia.org/)
* 0.00230%: [Tatoeba](https://tatoeba.org/en/)


## 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},
}
```