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+
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+ ---
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+ license: apache-2.0
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+ language:
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+ - aze
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+ - azb
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+ datasets:
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+ - allenai/nllb
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+ - oscar-corpus/OSCAR-2109
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+ - cis-lmu/Glot500
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ tags:
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+ - goldfish
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+
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+ ---
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+
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+ # azb_arab_100mb
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+
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+ Goldfish is a suite of monolingual language models trained for 350 languages.
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+ This model is the <b>South Azerbaijani</b> (Arabic script) model trained on 100MB of data, after accounting for an estimated byte premium of 1.49; content-matched text in South Azerbaijani takes on average 1.49x as many UTF-8 bytes to encode as English.
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+ 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).
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+
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+ Note: azb_arab is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. Macrolanguage code aze_arab (Azerbaijani) is included in Goldfish. Consider using that model depending on your use case.
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+
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+ 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).
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+
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+ Training code and sample usage: https://github.com/tylerachang/goldfish
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+
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+ Sample usage also in this Google Colab: [link](https://colab.research.google.com/drive/1rHFpnQsyXJ32ONwCosWZ7frjOYjbGCXG?usp=sharing)
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+
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+ ## Model details:
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+
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+ To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/model_details.json.
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+ All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
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+ Details for this model specifically:
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+
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+ * Architecture: gpt2
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+ * Parameters: 124770816
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+ * Maximum sequence length: 512 tokens
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+ * Training text data (raw): 149.04MB
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+ * Training text data (byte premium scaled): 100.005MB
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+ * Training tokens: 25029120 (x10 epochs)
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+ * Vocabulary size: 50000
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+ * Compute cost: 1.27782777913344e+17 FLOPs or ~12.1 NVIDIA A6000 GPU hours
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+
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+ Training datasets (percentages prior to deduplication):
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+ * 46.02648%: [NLLB (CommonCrawl and ParaCrawl)](https://huggingface.co/datasets/allenai/nllb)
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+ * 42.37928%: [Wikipedia 2023/08](https://dumps.wikimedia.org/)
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+ * 11.52167%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)
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+ * 0.07257%: [Glot500](https://huggingface.co/datasets/cis-lmu/Glot500), including [OSCAR](https://oscar-project.org/), [WikiMatrix](https://github.com/facebookresearch/LASER/tree/main/tasks/WikiMatrix)
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+
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+
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+ ## Citation
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+
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+ If you use this model, please cite:
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+
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+ ```
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+ @article{chang-etal-2024-goldfish,
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+ title={Goldfish: Monolingual Language Models for 350 Languages},
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+ author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.},
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+ journal={Preprint},
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+ year={2024},
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+ }
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+ ```