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TL;DR: In this article, we demonstrate a no-code approach to building a text classifier that not only outperforms Large Language Models (LLMs) such as OpenAI an
by enqush 3y ago
TL;DR: In this article, we demonstrate a no-code approach to building a text classifier that not only outperforms Large Language Models (LLMs) such as OpenAI and Cohere, but also does so with just a handful of labeled data. If you're eager to see the results, feel free to jump straight to the Experiments section.
- PaulHoule 3y agoSo it sounds like you have a UI for building a training set and also a model trainer, right?
- enqush 3y agoThat is correct! our platform provides a seamless UI for constructing training sets and training models. Behind the scenes, there's a lot going on: - Data ingestion: from various platforms, starting with Google Drive and soon expanding to OneDrive and Dropbox. - A robust labeling backend: with native integration with LabelStudio. - Range of GPU selection: for training, starting from the affordable RTX A2000 to the powerful H100, starting at just $0.17/hour. You can save up to 60% compared to AWS, depending on the GPU. - Model deployment & optimization: All trained models are fine-tuned using NVIDIA TensorRT and Triton for maximum efficiency.