Summary of Autotrain: No-code Training For State-of-the-art Models, by Abhishek Thakur
AutoTrain: No-code training for state-of-the-art models
by Abhishek Thakur
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces AutoTrain, an open-source tool for training (or finetuning) models on custom datasets across various modalities and tasks. This no-code library simplifies the process of developing tailored solutions for industrial or open-source applications. AutoTrain supports a range of tasks, including large language model finetuning, text classification, token classification, sequence-to-sequence tasks, and more. The tool is available at https://github.com/huggingface/autotrain-advanced and can be used in local mode or on cloud machines. It also integrates with tens of thousands of shared models on the Hugging Face Hub and their variations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to develop custom solutions for specific tasks or industries. The authors created a tool called AutoTrain that lets you train (or fine-tune) models without needing to write code. This tool works with many different types of data, such as text, images, and tables. It’s free and open-source, so anyone can use it. You can even use it on your own computer or in the cloud. The goal is to help people develop solutions that are tailored to their specific needs. |
Keywords
» Artificial intelligence » Classification » Large language model » Text classification » Token
