goldfish-models / que_latn_full

huggingface.co
Total runs: 38
24-hour runs: 0
7-day runs: 5
30-day runs: 18
Model's Last Updated: August 27 2024
text-generation

Introduction of que_latn_full

Model Details of que_latn_full

que_latn_full

Goldfish is a suite of monolingual language models trained for 350 languages. This model is the Quechua (Latin script) model trained on 139MB of data (all our data in the language), after accounting for an estimated byte premium of 1.21; content-matched text in Quechua takes on average 1.21x 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: que_latn is a macrolanguage code. Individual language codes quz_latn (Cusco Quechua) and quy_latn (Ayacucho Quechua) are included in Goldfish, although with less data.

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. Details for this model specifically:

  • Architecture: gpt2
  • Parameters: 124770816
  • Maximum sequence length: 512 tokens
  • Training text data (raw): 169.32MB
  • Training text data (byte premium scaled): 139.385MB
  • Training tokens: 40595968 (x10 epochs)
  • Vocabulary size: 50000
  • Compute cost: 2.07152584261632e+17 FLOPs or ~19.6 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):

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},
}

Runs of goldfish-models que_latn_full on huggingface.co

38
Total runs
0
24-hour runs
-1
3-day runs
5
7-day runs
18
30-day runs

More Information About que_latn_full huggingface.co Model

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que_latn_full huggingface.co

que_latn_full huggingface.co is an AI model on huggingface.co that provides que_latn_full's model effect (), which can be used instantly with this goldfish-models que_latn_full model. huggingface.co supports a free trial of the que_latn_full model, and also provides paid use of the que_latn_full. Support call que_latn_full model through api, including Node.js, Python, http.

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https://huggingface.co/goldfish-models/que_latn_full

que_latn_full install

que_latn_full is an open source model from GitHub that offers a free installation service, and any user can find que_latn_full on GitHub to install. At the same time, huggingface.co provides the effect of que_latn_full install, users can directly use que_latn_full installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

que_latn_full install url in huggingface.co:

https://huggingface.co/goldfish-models/que_latn_full

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