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goldfish-models/pcm_latn_5mb

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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license: apache-2.0 language:

—pcm datasets:
—castorini/afriberta-corpus
—cis-lmu/Glot500
—castorini/afriberta-corpus libraryname: transformers pipelinetag: text-generation tags:
—goldfish
—arxiv:2408.10441 ---

pcmlatn5mb

Goldfish is a suite of monolingual language models trained for 350 languages. This model is the <b>Nigerian Pidgin</b> (Latin script) model trained on 5MB of data, after accounting for an estimated byte premium of 0.95; content-matched text in Nigerian Pidgin takes on average 0.95x 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: pcmlatn is an [individual language](https://iso639-3.sil.org/codetables/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).

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

Sample usage also in this Google Colab: link

Model details:

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. For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)! Details for this model specifically:

—Architecture: gpt2
—Parameters: 30698496
—Maximum sequence length: 512 tokens
—Training text data (raw): 4.77MB
—Training text data (byte premium scaled): 5.005MB
—Training tokens: 1119744 (x10 epochs)
—Vocabulary size: 33821
—Compute cost: 846809797754880.0 FLOPs or ~0.1 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):

—48.40160%: AfriBERTa
—48.06214%: Glot500, including AfriBERTa, AfroMAFT, Wortschatz Leipzig Data
—3.19131%: eBible
—0.34495%: Wikipedia 2023/08

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},
  url={https://www.arxiv.org/abs/2408.10441},
}