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        <title>AutoTrain: No-code training for state-of-the-art models</title>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations</title>
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            <namePart type="given">Delia</namePart>
            <namePart type="given">Irazu</namePart>
            <namePart type="family">Hernandez Farias</namePart>
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            <namePart type="family">Hope</namePart>
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        <name type="personal">
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    <abstract>With the advancements in open-source models, training(or finetuning) models on custom datasets has become a crucial part of developing solutions which are tailored to specific industrial or open-source applications. Yet, there is no single tool which simplifies the process of training across different types of modalities or tasks.We introduce AutoTrain(aka AutoTrain Advanced)—an open-source, no code tool/library which can be used to train (or finetune) models for different kinds of tasks such as: large language model (LLM) finetuning, text classification/regression, token classification, sequence-to-sequence task, finetuning of sentence transformers, visual language model (VLM) finetuning, image classification/regression and even classification and regression tasks on tabular data. AutoTrain Advanced is an open-source library providing best practices for training models on custom datasets. The library is available at https://github.com/huggingface/autotrain-advanced. AutoTrain can be used in fully local mode or on cloud machines and works with tens of thousands of models shared on Hugging Face Hub and their variations.</abstract>
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%0 Conference Proceedings
%T AutoTrain: No-code training for state-of-the-art models
%A Thakur, Abhishek
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F thakur-2024-autotrain
%X With the advancements in open-source models, training(or finetuning) models on custom datasets has become a crucial part of developing solutions which are tailored to specific industrial or open-source applications. Yet, there is no single tool which simplifies the process of training across different types of modalities or tasks.We introduce AutoTrain(aka AutoTrain Advanced)—an open-source, no code tool/library which can be used to train (or finetune) models for different kinds of tasks such as: large language model (LLM) finetuning, text classification/regression, token classification, sequence-to-sequence task, finetuning of sentence transformers, visual language model (VLM) finetuning, image classification/regression and even classification and regression tasks on tabular data. AutoTrain Advanced is an open-source library providing best practices for training models on custom datasets. The library is available at https://github.com/huggingface/autotrain-advanced. AutoTrain can be used in fully local mode or on cloud machines and works with tens of thousands of models shared on Hugging Face Hub and their variations.
%U https://aclanthology.org/2024.emnlp-demo.44
%P 419-423

Markdown (Informal)

[AutoTrain: No-code training for state-of-the-art models](https://aclanthology.org/2024.emnlp-demo.44) (Thakur, EMNLP 2024)

• AutoTrain: No-code training for state-of-the-art models (Thakur, EMNLP 2024)

ACL

• Abhishek Thakur. 2024. AutoTrain: No-code training for state-of-the-art models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 419–423, Miami, Florida, USA. Association for Computational Linguistics.