structured_data_extraction_with_llms_using_numind_and_got_OCR.ipynb

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    "# Structured data extraction with LLMs using NuMind NuExtract and GOT OCR 2.0\n",
    "\n",
    "In this notebook we will show how to use [GOT OCR 2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0) for extraction relevant texts from images. After that, we will be using [NuMind](https://numind.ai/blog/nuextract-a-foundation-model-for-structured-extraction) to perform structured data extraction on top of the extracted texts.\n",
    "\n",
    "General OCR Theory was introduced along with an excellent model, namely GOT, to promote the arrival of OCR-2.0. The GOT model, with 580M parameters, is a unified, elegant, and end-to-end model, consisting of a high-compression encoder and a long-contexts decoder. As an OCR-2.0 model, GOT can handle all the above \"characters\" under various OCR tasks. On the input side, the model supports commonly used scene- and document-style images in slice and whole-page styles. On the output side, GOT can generate plain or formatted results (markdown/tikz/smiles/kern) via an easy prompt. Besides, the model enjoys interactive OCR features, i.e., region-level recognition guided by coordinates or colors. Furthermore, we also adapt dynamic resolution and multi-page OCR technologies to GOT for better practicality. In experiments, we provide sufficient results to prove the superiority of our model.\n",
    "\n",
    "NuExtract, a lightweight text-to-JSON LLM. NuExtract allows to extract arbitrarily complex information from text and turns it into structured data. This model can be directly used in a zero-shot setting or fine-tuned to solve a specific extraction problem. As usual with NuMind, it has been open sourced under MIT license for everyone to use."
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    "## Getting started\n",
    "\n",
    "### Deploy the Argilla server¶\n",
    "\n",
    "If you already have deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following [this guide](https://docs.argilla.io/latest/getting_started/quickstart/).\n",
    "\n",
    "### Install dependencies"
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