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vec_latn_5mb

Text Generation
Transformers
PyTorch
Venetian
gpt2
goldfish
text-generation-inference
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vec_latn_5mb
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---
license: apache-2.0
language:
- vec
datasets:
- allenai/nllb
- cis-lmu/Glot500
- legacy-datasets/wikipedia
- allenai/MADLAD-400
- oscar-corpus/OSCAR-2109
library_name: transformers
pipeline_tag: text-generation
tags:
- goldfish

---

# vec_latn_5mb

Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the <b>Venetian</b> (Latin script) model trained on 5MB of data, after accounting for an estimated byte premium of 1.00; content-matched text in Venetian takes on average 1.00x 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: vec_latn 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 latn).

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: 39087104
* Maximum sequence length: 512 tokens
* Training text data (raw): 4.98MB
* Training text data (byte premium scaled): 5.005MB
* Training tokens: 1401344 (x10 epochs)
* Vocabulary size: 50000
* Compute cost: 1059180505989120.0 FLOPs or ~0.1 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):
* 34.87927%: [NLLB (CommonCrawl and ParaCrawl)](https://huggingface.co/datasets/allenai/nllb)
* 33.55145%: [Glot500](https://huggingface.co/datasets/cis-lmu/Glot500), including [Wortschatz Leipzig Data](https://wortschatz.uni-leipzig.de/en/download), [NLLB_seed](https://github.com/facebookresearch/flores/blob/main/nllb_seed/README.md), [OSCAR](https://oscar-project.org/), [W2C](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0022-6133-9), [Wikipedia Hugging Face](https://huggingface.co/datasets/legacy-datasets/wikipedia)
* 21.45405%: [MADLAD-400 (CommonCrawl)](https://huggingface.co/datasets/allenai/MADLAD-400)
* 10.10163%: [Wikipedia 2023/08](https://dumps.wikimedia.org/)
* 0.01360%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)


## 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},
}
```