google / umt5-small

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Model's Last Updated: July 06 2023
text2text-generation

Introduction of umt5-small

Model Details of umt5-small

Google's UMT5

UMT5 is pretrained on the an updated version of mC4 corpus, covering 107 languages:

Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.

Note : UMT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.

Pretraining Dataset: mC4

Other Community Checkpoints: here

Paper: UniMax, Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining

Authors: by Hyung Won Chung, Xavier Garcia, Adam Roberts, Yi Tay, Orhan Firat, Sharan Narang, Noah Constant

Abstract

Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.

Runs of google umt5-small on huggingface.co

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More Information About umt5-small huggingface.co Model

More umt5-small license Visit here:

https://choosealicense.com/licenses/apache-2.0

umt5-small huggingface.co

umt5-small huggingface.co is an AI model on huggingface.co that provides umt5-small's model effect (), which can be used instantly with this google umt5-small model. huggingface.co supports a free trial of the umt5-small model, and also provides paid use of the umt5-small. Support call umt5-small model through api, including Node.js, Python, http.

umt5-small huggingface.co Url

https://huggingface.co/google/umt5-small

google umt5-small online free

umt5-small huggingface.co is an online trial and call api platform, which integrates umt5-small's modeling effects, including api services, and provides a free online trial of umt5-small, you can try umt5-small online for free by clicking the link below.

google umt5-small online free url in huggingface.co:

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umt5-small install

umt5-small is an open source model from GitHub that offers a free installation service, and any user can find umt5-small on GitHub to install. At the same time, huggingface.co provides the effect of umt5-small install, users can directly use umt5-small installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

umt5-small install url in huggingface.co:

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umt5-small huggingface.co Url

Provider of umt5-small huggingface.co

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