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| "cell_type": "markdown", | |
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| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/firmai/a73e41922afe9454e33f27ed82fa87da/bertopic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
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| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "AXHLDxJdRzBi" | |
| }, | |
| "source": [ | |
| "Topic Modeling with BERTopic\n", | |
| "\n", | |
| "## BERTopic\n", | |
| "BERTopic is a topic modeling technique that leverages 🤗 transformers and a custom class-based TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. \n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "130PIKarkY1_" | |
| }, | |
| "source": [ | |
| "# Enabling the GPU\n", | |
| "\n", | |
| "First, you'll need to enable GPUs for the notebook:\n", | |
| "\n", | |
| "- Navigate to Edit→Notebook Settings\n", | |
| "- select GPU from the Hardware Accelerator drop-down\n", | |
| "\n", | |
| "[Reference](https://colab.research.google.com/notebooks/gpu.ipynb)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "lj2MUI9Mkdxu" | |
| }, | |
| "source": [ | |
| "# **Installing BERTopic**\n", | |
| "\n", | |
| "We start by installing BERTopic from PyPi:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!git clone https://gist.github.com/c1a8c0359fbde2f6dcb92065b8ffc5e3.git" | |
| ], | |
| "metadata": { | |
| "id": "8w4DtyrYLMkN", | |
| "outputId": "ced21fa5-870e-47fc-ef44-2f64206c9271", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Cloning into 'c1a8c0359fbde2f6dcb92065b8ffc5e3'...\n", | |
| "remote: Enumerating objects: 3, done.\u001b[K\n", | |
| "remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 3\u001b[K\n", | |
| "Unpacking objects: 100% (3/3), done.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "SNa-KtKDRnus" | |
| }, | |
| "source": [ | |
| "%%capture\n", | |
| "!pip install bertopic" | |
| ], | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0ehO3emgk96g" | |
| }, | |
| "source": [ | |
| "## Restart the Notebook\n", | |
| "After installing BERTopic, some packages that were already loaded were updated and in order to correctly use them, we should now restart the notebook.\n", | |
| "\n", | |
| "From the Menu:\n", | |
| "\n", | |
| "Runtime → Restart Runtime" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "Y3VGFZ1USMTu" | |
| }, | |
| "source": [ | |
| "# Data\n", | |
| "For this example, we use the popular 20 Newsgroups dataset which contains roughly 18000 newsgroups posts" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "id": "JJij3WP6SEQD" | |
| }, | |
| "source": [ | |
| "from sklearn.datasets import fetch_20newsgroups\n", | |
| "docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']" | |
| ], | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "len(docs)" | |
| ], | |
| "metadata": { | |
| "id": "FUzbI4MSLqXJ", | |
| "outputId": "b2d6f458-2380-49a7-f075-39c03f31657b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "18846" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "SBcNmZJzSTY8" | |
| }, | |
| "source": [ | |
| "# **Topic Modeling**\n", | |
| "\n", | |
| "In this example, we will go through the main components of BERTopic and the steps necessary to create a strong topic model. \n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "QI6vwelqnTL-" | |
| }, | |
| "source": [ | |
| "## Training\n", | |
| "\n", | |
| "We start by instantiating BERTopic. We set language to `english` since our documents are in the English language. If you would like to use a multi-lingual model, please use `language=\"multilingual\"` instead. \n", | |
| "\n", | |
| "We will also calculate the topic probabilities. However, this can slow down BERTopic significantly at large amounts of data (>100_000 documents). It is advised to turn this off if you want to speed up the model. \n" | |
| ] | |
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| } | |
| }, | |
| "source": [ | |
| "from bertopic import BERTopic\n", | |
| "\n", | |
| "topic_model = BERTopic(language=\"english\", calculate_probabilities=True, verbose=True)" | |
| ], | |
| "execution_count": 7, | |
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| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "version_major": 2, | |
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| } | |
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| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "2022-04-22 21:31:22,926 - BERTopic - Transformed documents to Embeddings\n", | |
| "/usr/local/lib/python3.7/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n", | |
| " warnings.warn(problem)\n", | |
| "2022-04-22 21:31:32,274 - BERTopic - Reduced dimensionality with UMAP\n", | |
| "2022-04-22 21:31:32,305 - BERTopic - Clustered UMAP embeddings with HDBSCAN\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "tJs94tXDo4f6" | |
| }, | |
| "source": [ | |
| "**NOTE**: Use `language=\"multilingual\"` to select a model that support 50+ languages." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "A5O3KpHTnVpz" | |
| }, | |
| "source": [ | |
| "## Extracting Topics\n", | |
| "After fitting our model, we can start by looking at the results. Typically, we look at the most frequent topics first as they best represent the collection of documents. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "## takes about 3 mins\n", | |
| "topics, probs = topic_model.fit_transform(docs)" | |
| ], | |
| "metadata": { | |
| "id": "5cNPfMcnMNcF", | |
| "outputId": "e11f9579-a4b9-4ab5-8e76-5097e0285ed6", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 102, | |
| "referenced_widgets": [ | |
| "bec29796139c47ffbdf7169eab332e03", | |
| "aa5bbd4366814e43ba25d0b2a3385664", | |
| "592456bc306e40769b8e9a2c2fb94878", | |
| "de392f97af2d443b8472bead2b633f89", | |
| "fcb24a87b30b483faca591e8a5df7dd6", | |
| "a5253de5ded149e1b4cdfef07897bc5e", | |
| "7273a69cb0d240ef86c842a09d040b24", | |
| "d85c643eed094004a375cf8c8e40331f", | |
| "d6d7f8814dc64d01858f47dabfbe936c", | |
| "d3744644e50c47be9742a11e590c8673", | |
| "5d0197df129f479aa59b8ba6f0bd2419" | |
| ] | |
| } | |
| }, | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "2022-04-22 21:33:09,318 - BERTopic - Transformed documents to Embeddings\n", | |
| "2022-04-22 21:33:46,528 - BERTopic - Reduced dimensionality with UMAP\n", | |
| "2022-04-22 21:34:19,156 - BERTopic - Clustered UMAP embeddings with HDBSCAN\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| }, | |
| "id": "ScBUgXn06IK6", | |
| "outputId": "5f7b1654-6e5e-44da-becd-e2f9366059d6" | |
| }, | |
| "source": [ | |
| "freq = topic_model.get_topic_info(); freq.head(5)" | |
| ], | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Topic Count Name\n", | |
| "0 -1 6738 -1_to_is_the_of\n", | |
| "1 0 1834 0_game_team_games_he\n", | |
| "2 1 610 1_key_clipper_chip_encryption\n", | |
| "3 2 527 2_ites_cheek_yep_huh\n", | |
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| "-1 refers to all outliers and should typically be ignored. Next, let's take a look at a frequent topic that were generated:" | |
| ] | |
| }, | |
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| "source": [ | |
| "topic_model.get_topic(0) # Select the most frequent topic" | |
| ], | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[('game', 0.010323378565553433),\n", | |
| " ('team', 0.008996576611664232),\n", | |
| " ('games', 0.0071690667098439406),\n", | |
| " ('he', 0.0069795563663820935),\n", | |
| " ('players', 0.006315426414352591),\n", | |
| " ('season', 0.006222095146519599),\n", | |
| " ('hockey', 0.006111358181489681),\n", | |
| " ('play', 0.005766479646814996),\n", | |
| " ('25', 0.005627978524653156),\n", | |
| " ('year', 0.005579878017160986)]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 17 | |
| } | |
| ] | |
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| "cell_type": "markdown", | |
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| "id": "ixc-X2JzodrZ" | |
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| "**NOTE**: BERTopic is stocastich which mmeans that the topics might differ across runs. This is mostly due to the stocastisch nature of UMAP." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "UbT2Bd9gqaJ3" | |
| }, | |
| "source": [ | |
| "# **Visualization**\n", | |
| "There are several visualization options available in BERTopic, namely the visualization of topics, probabilities and topics over time. Topic modeling is, to a certain extent, quite subjective. Visualizations help understand the topics that were created. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "M8c8LenB8Zyl" | |
| }, | |
| "source": [ | |
| "## Visualize Topics\n", | |
| "After having trained our `BERTopic` model, we can iteratively go through perhaps a hundred topic to get a good \n", | |
| "understanding of the topics that were extract. However, that takes quite some time and lacks a global representation. \n", | |
| "Instead, we can visualize the topics that were generated in a way very similar to \n", | |
| "[LDAvis](https://github.com/cpsievert/LDAvis):" | |
| ] | |
| }, | |
| { | |
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| "outputId": "77a4d223-9a3d-4368-cdcb-97e8b4b87663" | |
| }, | |
| "source": [ | |
| "topic_model.visualize_topics()" | |
| ], | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
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