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"accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Newsgroup Clustering with LLM Embeddings\n", + "\n", + "In the [newsgroup demo](./demo1_doc_cluster.ipynb) we showed how to cluster newsgroups by representing each text via a simple TF-IDF score. In this demo, we will use a more sophisticated embedding using LLMs. LLMs can provide a powerful pre-processor for a variety of text-related tasks. In going through this demo, you will learn to:\n", + "\n", + "* Download a simple, pre-trained LLM model from the Hugging Face platform\n", + "* Apply pre-trained LLM embeddings to a text corpus\n", + "* Use the LLM embeddings for a downstream task (in this case, clustering)\n", + "\n", + "## Downloading the MiniLM model from Hugging Face\n", + "\n", + "In this demo, we will use Microsoft's excellent [MiniLM model](https://github.com/microsoft/unilm/blob/master/minilm/README.md). The MiniLM is a remarkably small model that you can run without a powerful GPU and still get decent performance. For example, you can run the model on Google colab, even with a CPU only. The MiniLM model is available on [Hugging Face](https://huggingface.co/). Hugging Face is a widely-used online platform that provides tools and resources to help people build and share AI models, datasets, and applications. You can use Hugging Face for your projects. \n", + "\n", + "To use the MiniLM model, we first install the `sentence-transformers` package that is part of [hugging Face](https://huggingface.co/sentence-transformers).\n" + ], + "metadata": { + "id": "Qqf0R1EJ-CM1" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U sentence-transformers\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "El-pHFoKfeip", + "outputId": "0fe1f37b-e833-486e-cf61-69f60a1e90a7" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.2.1)\n", + "Collecting sentence-transformers\n", + " Downloading sentence_transformers-3.3.1-py3-none-any.whl.metadata (10 kB)\n", + "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.46.2)\n", 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requests->huggingface-hub>=0.20.0->sentence-transformers) (2.2.3)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2024.8.30)\n", + "Downloading sentence_transformers-3.3.1-py3-none-any.whl (268 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: sentence-transformers\n", + " Attempting uninstall: sentence-transformers\n", + " Found existing installation: sentence-transformers 3.2.1\n", + " Uninstalling sentence-transformers-3.2.1:\n", + " Successfully uninstalled sentence-transformers-3.2.1\n", + "Successfully installed sentence-transformers-3.3.1\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The mdoels in Hugging Face are mostly available in PyTorch, so we need to import `torch` to use them. This demo will run faster if you enable the GPU. If you are on Google Colab, you can select the GPU by following these [instructions](https://medium.com/@208r1a6734/how-to-connect-the-gpu-in-google-colab-5ff4cbd4b0a1). But, if you only have a CPU, don't worry -- the demo will still work." + ], + "metadata": { + "id": "s-TAuSS7pHf3" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "print(device)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "alMBuI4o1sEA", + "outputId": "71eef463-4653-41e5-a242-cc0b28efef13" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cuda\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Next, we download a `SentenceTransformer` model. [Sentence transformers](https://huggingface.co/sentence-transformers) use LLMs to convert short text to an embedding, which is a numerical vector. That numerical vector can then be the input for some subsequent neural network. In this case, we download the miniLM sentence transformer." + ], + "metadata": { + "id": "8vXOlxb0q4Do" + } + }, + { + "cell_type": "code", + "source": [ + "from sentence_transformers import SentenceTransformer\n", + "sentences = [\"This is an example sentence\", \"Each sentence is converted\"]\n", + "\n", + "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')\n", + "model = model.to(device)\n", + "embeddings = model.encode(sentences)" + ], + "metadata": { + "id": "q-_NWS3sP6MS", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 473, + "referenced_widgets": [ + "a38ce54ee96b4789a1d5606953d6c190", + "ff262286b3e5490eb8cdc08060aa8ed9", + "b991357547024496b8130705f1c9eff3", + "4e9b3b558dfe4da0ba701696353edfeb", + "c09f5bb925764ba0a08206ae15e6ff23", + "cd098cb779694cd995e7090c5966cae2", + "4a11fc3ad8984119af17138f602b3e78", + "c07b5773df5c4857a5e25b2b79f27f0d", + "20f2d798dfb54817867b8ee34af664cf", + "df873f66155c41f085963828910ba5c9", + 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