The free text in this dataset is not entirely clean. Of interest here are just the text of the reviews, the headline, and just those in the "cameras" category, naturally. You've collected hundreds of thousands of reviews along with user-provided headlines (which is a sort of 'summary' of a review) and want to use LLMs to create these product summaries.Īs a stand-in for such a dataset, this example will use the Amazon Customer Review dataset, containing 130 million product reviews from Amazon customers. Users leave reviews on products, and you think it would be nice to condense all reviews for a product into one summary for customers, rather than have them sift through a hundred reviews. Let's imagine you run an e-commerce site selling camera products. It's not hard to fine-tune even an 11 billion parameter model on Databricks – if that is what is necessary! Problem: Summarizing Product Reviews It will also introduce Microsoft's DeepSpeed to accelerate fine-tuning of very large language models. It will use Hugging Face on Databricks, including its MLflow integration. This blog will explore easy fine-tuning of the T5 family of language models, from its smallest to largest size, to specialize it for a simple use case: constructing a product review overview from many product reviews. However, sometimes it's valuable or essential to "fine-tune" these models to perform better on a specific task. Off-the-shelf, pre-trained, LLMs like T5 and BERT can work well for a wide range of real-world problems, without additional data or training. However, this is merely one of several advances in transformer-based models, many others of which are open and readily available for tasks like translation, classification, and summarization - not just chat.Ī previous blog explored the basics of accessing these models on Databricks via the popular Hugging Face transformers library. Many are wondering how to take advantage of models like this in their own applications. Large language models (LLMs) are currently in the spotlight following the sensational release of ChatGPT.
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