Based on the medium’s new policies, I am going to start with a series of short articles that deal with only practical aspects of various LLM-related software.
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The Tutorial
In this tutorial, we will learn how to extract structured data from free text. Let's get some data.
# Get some text https://arxiv.org/abs/2308.03279 abstract inp = """Large language models (LLMs) have demonstrated remarkable \ generalizability, such as understanding arbitrary entities and relations. \ Instruction tuning has proven effective for distilling LLMs \ into more cost-efficient models such as Alpaca and Vicuna. \ Yet such student models still trail the original LLMs by \ large margins in downstream applications. In this paper, \ we explore targeted distillation with mission-focused instruction \ tuning to train student models that can excel in a broad application \ class such as open information extraction. Using named entity \ recognition (NER) for case study, we show how ChatGPT can be distilled \ into much smaller UniversalNER models for open NER. For evaluation,\ we assemble the largest NER benchmark to date, comprising 43 datasets \ across 9 diverse domains such as biomedicine, programming, social media, \ law, finance. Without using any direct supervision, UniversalNER \ attains remarkable NER accuracy across tens of thousands of entity \ types, outperforming general instruction-tuned models such as Alpaca \ and Vicuna by over 30 absolute F1 points in average. With a tiny \