diff --git a/tasks/clustering/LaserClusteringExample.ipynb b/tasks/clustering/LaserClusteringExample.ipynb
index df74c6d2..65256ab2 100644
--- a/tasks/clustering/LaserClusteringExample.ipynb
+++ b/tasks/clustering/LaserClusteringExample.ipynb
@@ -1,6107 +1,540 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EqqG01vB6E9H"
- },
- "source": [
- "# Clustering Multilingual Embeddings using LASER\n",
- "\n",
- "In this tutorial, we'll explore the power of Language-Agnostic SEntence Representations ([LASER](https://github.com/facebookresearch/LASER)) for generating multilingual embeddings. We'll then use these embeddings to perform clustering on the [MASSIVE](https://github.com/alexa/massive) dataset. Our goal is to show that LASER embeddings can effectively group texts not only by their thematic content but also across different languages. LASER can encode sentences from multiple languages into a shared embedding space, allowing for cross-lingual understanding and comparison. We'll see how this capability is useful for tasks like multilingual embeddings clustering.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "eDx4HES46JTf"
- },
- "source": [
- "## Setting Up Your Environment\n",
- "\n",
- "We will use the following libraries:\n",
- "- `scikit-learn` for clustering algorithms and evaluation metrics.\n",
- "- `plotly` for generating interactive plots.\n",
- "- `laser_encoders` for generating text embeddings.\n",
- "- `datasets` for downloading data from huggingface hub.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "EP-noK6R6xU0"
- },
- "outputs": [],
- "source": [
- "!pip install -q laser_encoders datasets"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "id": "vWPUBx6u6A2u"
- },
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "from sklearn.cluster import KMeans\n",
- "from sklearn import metrics\n",
- "from sklearn.manifold import TSNE\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "from laser_encoders import LaserEncoderPipeline\n",
- "\n",
- "import datasets\n",
- "from datasets import load_dataset\n",
- "\n",
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "tFlfrpM3nTuA"
- },
- "source": [
- "## Loading the MASSIVE Dataset\n",
- "We'll use the MASSIVE dataset, which includes texts in 52 languages. Let's load a subset of this dataset for our experiment:\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
+ "nbformat": 4,
+ "nbformat_minor": 0,
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- ]
+ "provenance": [],
+ "gpuType": "T4"
},
- "id": "zTQQWUVmSMaI",
- "outputId": "6f5ad551-4c24-47bc-f09a-2ec7cc3ad9aa"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "0eacb4420b2d45749c2b49855ebaf383",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Generating train split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "3bcbdbd8ef9d4319aeb3f02e6bc35a0e",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Generating validation split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "3ad7664000a4424f85d13f0f2994e49e",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Generating test split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "language_info": {
+ "name": "python"
},
+ "accelerator": "GPU"
+ },
+ "cells": [
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "133cb3179a22421a85e13287af7dd12a",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Generating train split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "cell_type": "markdown",
+ "source": [
+ "# Clustering Multilingual Embeddings using LASER\n",
+ "\n",
+ "In this tutorial, we'll explore the power of Language-Agnostic SEntence Representations ([LASER](https://github.com/facebookresearch/LASER)) for generating multilingual embeddings. We'll then use these embeddings to perform clustering on the [MASSIVE](https://github.com/alexa/massive) dataset. Our goal is to show that LASER embeddings can effectively group texts not only by their thematic content but also across different languages. LASER can encode sentences from multiple languages into a shared embedding space, allowing for cross-lingual understanding and comparison. We'll see how this capability is useful for tasks like multilingual clustering.\n"
+ ],
+ "metadata": {
+ "id": "EqqG01vB6E9H"
+ }
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "44b7b55739904147be99c41e0802cfe4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Generating validation split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "cell_type": "markdown",
+ "source": [
+ "## Setting Up Your Environment\n",
+ "\n",
+ "We will use the following libraries:\n",
+ "- `scikit-learn` for clustering algorithms and evaluation metrics.\n",
+ "- `plotly` for generating interactive plots.\n",
+ "- `laser_encoders` for generating text embeddings.\n",
+ "- `datasets` for downloading data from huggingface hub.\n"
+ ],
+ "metadata": {
+ "id": "eDx4HES46JTf"
+ }
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "735958954b1e4d93a9560c12445d82fe",
- "version_major": 2,
- "version_minor": 0
+ "cell_type": "code",
+ "source": [
+ "!pip install -q laser_encoders datasets"
+ ],
+ "metadata": {
+ "id": "EP-noK6R6xU0"
},
- "text/plain": [
- "Generating test split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "execution_count": null,
+ "outputs": []
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "d4e4f7bf09f24b16a6fad76b59c8c9eb",
- "version_major": 2,
- "version_minor": 0
+ "cell_type": "code",
+ "source": [
+ "!pip install -U kaleido"
+ ],
+ "metadata": {
+ "id": "j4OXMzQvzZtb",
+ "outputId": "0c3b6473-a59d-4e74-bf25-64cf45e8d3f1",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
},
- "text/plain": [
- "Generating train split: 0 examples [00:00, ? examples/s]"
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: kaleido in /usr/local/lib/python3.10/dist-packages (0.2.1)\n"
+ ]
+ }
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6a9f4a172be042639fc58f493e6d3cbb",
- "version_major": 2,
- "version_minor": 0
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "vWPUBx6u6A2u"
},
- "text/plain": [
- "Generating validation split: 0 examples [00:00, ? examples/s]"
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn.cluster import KMeans\n",
+ "from sklearn import metrics\n",
+ "from sklearn.manifold import TSNE\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from laser_encoders import LaserEncoderPipeline\n",
+ "\n",
+ "import datasets\n",
+ "from datasets import load_dataset\n",
+ "\n",
+ "import warnings\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n"
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "48284f06ee714f53b2d87a51afe9bfc8",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Generating test split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "cell_type": "markdown",
+ "source": [
+ "## Loading the MASSIVE Dataset\n",
+ "We'll use the MASSIVE dataset, which includes texts in 52 languages. Let's load a subset of this dataset for our experiment:\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "tFlfrpM3nTuA"
+ }
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "449acb6cf785447dbd524da69a01f19d",
- "version_major": 2,
- "version_minor": 0
+ "cell_type": "code",
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "n = 20\n",
+ "eng_data = load_dataset(\"AmazonScience/massive\", \"en-US\", split=f'train[:{n}]')\n",
+ "fra_data = load_dataset(\"AmazonScience/massive\", \"fr-FR\", split=f'train[:{n}]')\n",
+ "ita_data = load_dataset(\"AmazonScience/massive\", \"it-IT\", split=f'train[:{n}]')\n",
+ "spa_data = load_dataset(\"AmazonScience/massive\", \"es-ES\", split=f'train[:{n}]')\n",
+ "jap_data = load_dataset(\"AmazonScience/massive\", \"ja-JP\", split=f'train[:{n}]')\n",
+ "arb_data = load_dataset(\"AmazonScience/massive\", \"ar-SA\", split=f'train[:{n}]')\n",
+ "chn_data = load_dataset(\"AmazonScience/massive\", \"zh-CN\", split=f'train[:{n}]')\n",
+ "afr_data = load_dataset(\"AmazonScience/massive\", \"af-ZA\", split=f'train[:{n}]')\n",
+ "rus_data = load_dataset(\"AmazonScience/massive\", \"ru-RU\", split=f'train[:{n}]')\n",
+ "hin_data = load_dataset(\"AmazonScience/massive\", \"hi-IN\", split=f'train[:{n}]')"
+ ],
+ "metadata": {
+ "id": "zTQQWUVmSMaI"
},
- "text/plain": [
- "Generating train split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "execution_count": null,
+ "outputs": []
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "be3be7c622f94d2c8dd64451667c8677",
- "version_major": 2,
- "version_minor": 0
+ "cell_type": "code",
+ "source": [
+ "data_dict = {\n",
+ " \"english\": eng_data[\"utt\"],\n",
+ " \"french\": fra_data[\"utt\"],\n",
+ " \"italian\": ita_data[\"utt\"],\n",
+ " \"spanish\": spa_data[\"utt\"],\n",
+ " \"japanese\": jap_data[\"utt\"],\n",
+ " \"arabic\": arb_data[\"utt\"],\n",
+ " \"chinese\": chn_data[\"utt\"],\n",
+ " \"afrikaans\": afr_data[\"utt\"],\n",
+ " \"russian\": rus_data[\"utt\"],\n",
+ " \"hindu\": hin_data[\"utt\"],\n",
+ "}"
+ ],
+ "metadata": {
+ "id": "sn-hexAdkkne"
},
- "text/plain": [
- "Generating validation split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "execution_count": null,
+ "outputs": []
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "b19577b5a17349f581fe83216a9f0b87",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Generating test split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "cell_type": "markdown",
+ "source": [
+ "Here what the dataset looks like."
+ ],
+ "metadata": {
+ "id": "CnPKEtYnkvaq"
+ }
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1a0a502d2f0e4879970cffd05ec6444f",
- "version_major": 2,
- "version_minor": 0
+ "cell_type": "code",
+ "source": [
+ "for lang, texts in data_dict.items():\n",
+ " print(f\"{lang}: {texts[0]}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "nwjzEPUqk4-9",
+ "outputId": "3b04501d-c642-4fa8-b746-1d607c390bb8"
},
- "text/plain": [
- "Generating train split: 0 examples [00:00, ? examples/s]"
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "english: wake me up at nine am on friday\n",
+ "french: réveille-moi à neuf heures du matin le vendredi\n",
+ "italian: svegliami alle nove di mattina venerdì\n",
+ "spanish: despiértame a las nueve de la mañana el viernes\n",
+ "japanese: 金曜日の午前九時に起こしてください\n",
+ "arabic: صحيني تسعة الصباح يوم الجمعة\n",
+ "chinese: 星期五早上九点叫醒我\n",
+ "afrikaans: maak my wakker nege-uur v. m. op vrydag\n",
+ "russian: разбуди меня в девять утра в пятницу\n",
+ "hindu: शुक्रवार को सुबह नौ बजे मुझे जगा दो\n"
+ ]
+ }
]
- },
- "metadata": {},
- "output_type": "display_data"
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "378ac7aa5779462ebf592504442f727f",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Generating validation split: 0 examples [00:00, ? examples/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "cell_type": "markdown",
+ "source": [
+ "## Generating LASER Embeddings\n",
+ "\n",
+ "We will encode our text data into embeddings using `laser_encoders`. This step is crucial as it translates our multilingual dataset into a uniform representation.\n",
+ "\n"
+ ],
+ "metadata": {
+ "id": "n7bl3lwnnp_Z"
+ }
},
{
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "aab860a2027d4418adf00f01d1648a6f",
- "version_major": 2,
- "version_minor": 0
+ "cell_type": "code",
+ "source": [
+ "combined_sentences = []\n",
+ "for senetence_list in data_dict.values():\n",
+ " combined_sentences.extend(senetence_list)\n",
+ "\n",
+ "print(f\"All together we have {len(combined_sentences)} sentences\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "_skE8eHnkpXV",
+ "outputId": "c3260a3a-21ab-46aa-9f78-91d08649ceb6"
},
- "text/plain": [
- "Generating test split: 0 examples [00:00, ? examples/s]"
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "All together we have 200 sentences\n"
+ ]
+ }
]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from datasets import load_dataset\n",
- "\n",
- "n = 20\n",
- "eng_data = load_dataset(\"AmazonScience/massive\", \"en-US\", split=f'train[:{n}]')\n",
- "fra_data = load_dataset(\"AmazonScience/massive\", \"fr-FR\", split=f'train[:{n}]')\n",
- "ita_data = load_dataset(\"AmazonScience/massive\", \"it-IT\", split=f'train[:{n}]')\n",
- "spa_data = load_dataset(\"AmazonScience/massive\", \"es-ES\", split=f'train[:{n}]')\n",
- "jap_data = load_dataset(\"AmazonScience/massive\", \"ja-JP\", split=f'train[:{n}]')\n",
- "arb_data = load_dataset(\"AmazonScience/massive\", \"ar-SA\", split=f'train[:{n}]')\n",
- "chn_data = load_dataset(\"AmazonScience/massive\", \"zh-CN\", split=f'train[:{n}]')\n",
- "afr_data = load_dataset(\"AmazonScience/massive\", \"af-ZA\", split=f'train[:{n}]')\n",
- "rus_data = load_dataset(\"AmazonScience/massive\", \"ru-RU\", split=f'train[:{n}]')\n",
- "hin_data = load_dataset(\"AmazonScience/massive\", \"hi-IN\", split=f'train[:{n}]')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "id": "sn-hexAdkkne"
- },
- "outputs": [],
- "source": [
- "data_dict = {\n",
- " \"english\": eng_data[\"utt\"],\n",
- " \"french\": fra_data[\"utt\"],\n",
- " \"italian\": ita_data[\"utt\"],\n",
- " \"spanish\": spa_data[\"utt\"],\n",
- " \"japanese\": jap_data[\"utt\"],\n",
- " \"arabic\": arb_data[\"utt\"],\n",
- " \"chinese\": chn_data[\"utt\"],\n",
- " \"afrikaans\": afr_data[\"utt\"],\n",
- " \"russian\": rus_data[\"utt\"],\n",
- " \"hindu\": hin_data[\"utt\"],\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CnPKEtYnkvaq"
- },
- "source": [
- "Here what the dataset looks like."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "nwjzEPUqk4-9",
- "outputId": "2626af9a-6318-472c-faf2-9d74e297957f"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "english: wake me up at nine am on friday\n",
- "french: réveille-moi à neuf heures du matin le vendredi\n",
- "italian: svegliami alle nove di mattina venerdì\n",
- "spanish: despiértame a las nueve de la mañana el viernes\n",
- "japanese: 金曜日の午前九時に起こしてください\n",
- "arabic: صحيني تسعة الصباح يوم الجمعة\n",
- "chinese: 星期五早上九点叫醒我\n",
- "afrikaans: maak my wakker nege-uur v. m. op vrydag\n",
- "russian: разбуди меня в девять утра в пятницу\n",
- "hindu: शुक्रवार को सुबह नौ बजे मुझे जगा दो\n"
- ]
- }
- ],
- "source": [
- "for lang, texts in data_dict.items():\n",
- " print(f\"{lang}: {texts[0]}\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "n7bl3lwnnp_Z"
- },
- "source": [
- "## Generating LASER Embeddings\n",
- "\n",
- "We will encode our text data into embeddings using `laser_encoders`. This step is crucial as it translates our multilingual dataset into a uniform representation.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
},
- "id": "_skE8eHnkpXV",
- "outputId": "7646dd4c-de73-41d3-84ab-852eda8c5a2b"
- },
- "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "All together we have 200 sentences\n"
- ]
- }
- ],
- "source": [
- "combined_sentences = []\n",
- "for senetence_list in data_dict.values():\n",
- " combined_sentences.extend(senetence_list)\n",
- "\n",
- "print(f\"All together we have {len(combined_sentences)} sentences\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_qJPz9lloNYR"
- },
- "source": [
- "When we initialize the encoder with `lang='eng'`, it automatically defaults to using LASER 2, which offers support for nearly 100 languages.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "id": "pgWrd9KIEHIw"
- },
- "outputs": [],
- "source": [
- "encoder = LaserEncoderPipeline(lang=\"eng\")\n",
- "embeddings = encoder.encode_sentences(combined_sentences, normalize_embeddings=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "qtXbMQ1_zz9y"
- },
- "source": [
- "## Clustering with Multilingual Embeddings\n",
- "With our LASER embeddings, we can now apply a clustering algorithm. K-Means is a good starting point for its simplicity and effectiveness:\n",
- "\n",
- "## Choice of number of clusters.\n",
- "In our case, we have `20` parallel sentences in each of 5 languages, meaning these sentences convey the same meanings or topics in different languages. If each sentence represents a unique topic or thematic content, then ideally, We would expect to see around 20 clusters. This is because LASER is designed to map semantically similar sentences to nearby points in the embedding space, regardless of the language."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "id": "kYvltGE_qpXy"
- },
- "outputs": [],
- "source": [
- "n_clusters = 20\n",
- "kmeans = KMeans(n_clusters=20, random_state=42)\n",
- "clusters = kmeans.fit_predict(embeddings)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "jHIyGdUC1B67"
- },
- "source": [
- "## Reduce Dimensionality for Visualization\n",
- "\n",
- "We'll use t-SNE to reduce the dimensionality of our embeddings so we can plot them in 2D:\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "id": "iYoDifv0lMNl"
- },
- "outputs": [],
- "source": [
- "tsne = TSNE(n_components=2, random_state=42)\n",
- "reduced_embeddings = tsne.fit_transform(embeddings)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QyqNtkxl2D8o"
- },
- "source": [
- "## Evaluating Clustering Performance\n",
- "To evaluate our clustering, we'll use metrics suited for clustering quality, such as the Adjusted Rand Index (ARI), Normalized Mutual Information Score and Silhouette Coefficient:\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
+ "cell_type": "markdown",
+ "source": [
+ "When we initialize the encoder with `lang='eng'`, it automatically defaults to using LASER 2, which offers support for nearly 100 languages.\n"
+ ],
+ "metadata": {
+ "id": "_qJPz9lloNYR"
+ }
},
- "id": "w7zgwToT2N9N",
- "outputId": "13e9f386-10bd-4146-b161-e231ff28f907"
- },
- "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rand Index: 0.9731155778894472\n",
- "Adjusted Rand Index: 0.7050871871580726\n",
- "Normalized Mutual Information: 0.862654325567496\n",
- "Silhouette Coefficient: 0.22755876183509827\n"
- ]
- }
- ],
- "source": [
- "from sklearn.metrics import rand_score,adjusted_rand_score, normalized_mutual_info_score, silhouette_score\n",
- "\n",
- "labels = list(range(20)) * len(data_dict)\n",
- "\n",
- "ri_score = rand_score(labels, clusters)\n",
- "ari_score = adjusted_rand_score(labels, clusters)\n",
- "nmi_score = normalized_mutual_info_score(labels, clusters, average_method='arithmetic')\n",
- "silhouette_avg = silhouette_score(embeddings, clusters)\n",
- "\n",
- "print(f\"Rand Index: {ri_score}\")\n",
- "print(f\"Adjusted Rand Index: {ari_score}\")\n",
- "print(f\"Normalized Mutual Information: {nmi_score}\")\n",
- "print(f\"Silhouette Coefficient: {silhouette_avg}\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "I4IxbYXz3m4Q"
- },
- "source": [
- "## Clustering Evaluation Summary with LASER Embeddings\n",
- "\n",
- "We evaluated our clustering model, which leverages LASER embeddings, with the following results:\n",
- "\n",
- "- **Rand Index (RI):** 0.973 - High accuracy, indicating successful clustering.\n",
- "- **Adjusted Rand Index (ARI):** 0.705 - Shows good performance, considering the adjustment for random chance.\n",
- "- **Normalized Mutual Information (NMI):** 0.862 - Demonstrates strong alignment between our clustering and the true labels.\n",
- "- **Silhouette Coefficient:** 0.227 - Suggests moderate cluster separation.\n",
- "\n",
- "### Role of LASER in Clustering Performance\n",
- "\n",
- "The effectiveness of our clustering, as evidenced by the high RI, ARI, and NMI scores, can be significantly attributed to the LASER embeddings. LASER's ability to create language-agnostic sentence representations has likely enhanced the clustering quality, enabling the algorithm to group sentences based on semantic similarity across different languages. This is further supported by the reasonable Silhouette Coefficient, indicating decent separation between clusters. These results showcase LASER’s capability in handling multilingual data.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "0msUmsZP1UMd"
- },
- "source": [
- "## Prepare Data for Plotting\n",
- "We organize our data for easy ploting using `plotly`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "id": "VVft-ZUMEtPP"
- },
- "outputs": [],
- "source": [
- "languages = []\n",
- "for lang, sentence in data_dict.items():\n",
- " languages.extend([lang] * len(sentence))\n",
- "\n",
- "# Create a DataFrame for plotting\n",
- "df = pd.DataFrame({\n",
- " 'TSNE Component 1': reduced_embeddings[:, 0],\n",
- " 'TSNE Component 2': reduced_embeddings[:, 1],\n",
- " 'Language': languages,\n",
- " 'Cluster': ['Cluster {}'.format(cluster) for cluster in clusters],\n",
- " 'Sentence': combined_sentences\n",
- "})\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pK5y0LUn1ln-"
- },
- "source": [
- "## Plot Using Plotly\n",
- "Le's then go ahead to create an interactive scatter plot:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "id": "6vu1UTQVmLu9"
- },
- "outputs": [],
- "source": [
- "import plotly.express as px\n",
- "import plotly.graph_objects as go\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 542
+ "cell_type": "code",
+ "source": [
+ "encoder = LaserEncoderPipeline(lang=\"eng\")\n",
+ "embeddings = encoder.encode_sentences(combined_sentences, normalize_embeddings=True)"
+ ],
+ "metadata": {
+ "id": "pgWrd9KIEHIw"
+ },
+ "execution_count": null,
+ "outputs": []
},
- "id": "0JG_MA7-lXMW",
- "outputId": "e45f02e8-3fa0-4b5f-ecb6-36b06dd998b0"
- },
- "outputs": [
{
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- "\n",
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = px.scatter(df, x='TSNE Component 1', y='TSNE Component 2', color='Language',\n",
- " hover_data=['Sentence'])\n",
- "fig.update_layout(title=\"Multilingual Sentence Embeddings Visualization\",\n",
- " xaxis_title=\"TSNE Component 1\", yaxis_title=\"TSNE Component 2\",\n",
- " legend_title=\"Language\")\n",
- "fig.show()\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "id": "FbKkpx5Eue0o"
- },
- "outputs": [],
- "source": [
- "num_languages = len(data_dict) # Number of languages\n",
- "num_sentences = len(reduced_embeddings) // num_languages\n",
- "\n",
- "cluster_centers = np.array([np.mean(reduced_embeddings[i::num_sentences], axis=0) for i in range(num_sentences)])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CLx-qUZR-Kf4"
- },
- "source": [
- "Let's try to draw circles to distinguish the clusters."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 542
+ "cell_type": "markdown",
+ "source": [
+ "## Clustering with Multilingual Embeddings\n",
+ "With our LASER embeddings, we can now apply a clustering algorithm. K-Means is a good starting point for its simplicity and effectiveness:\n",
+ "\n",
+ "## Choice of number of clusters.\n",
+ "In our case, we have `20` parallel sentences in each of 5 languages, meaning these sentences convey the same meanings or topics in different languages. If each sentence represents a unique topic or thematic content, then ideally, We would expect to see around 20 clusters. This is because LASER is designed to map semantically similar sentences to nearby points in the embedding space, regardless of the language."
+ ],
+ "metadata": {
+ "id": "qtXbMQ1_zz9y"
+ }
},
- "id": "mkpQlPP4unaU",
- "outputId": "3610200a-1e26-4da2-9879-e6a53a4dc96a"
- },
- "outputs": [
{
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- "\n",
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "radius = 1.5\n",
- "# Your existing code for the scatter plot\n",
- "fig = px.scatter(df, x='TSNE Component 1', y='TSNE Component 2', color='Language',\n",
- " hover_data=['Sentence'])\n",
- "# Add circles\n",
- "for center in cluster_centers:\n",
- " fig.add_trace(go.Scatter(x=[center[0]], y=[center[1]],\n",
- " mode='markers',\n",
- " marker=dict(size=10, color='LightSkyBlue'),\n",
- " showlegend=False))\n",
- " fig.add_shape(type=\"circle\",\n",
- " xref=\"x\", yref=\"y\",\n",
- " x0=center[0] - radius, y0=center[1] - radius,\n",
- " x1=center[0] + radius, y1=center[1] + radius,\n",
- " line_color=\"LightSeaGreen\", opacity=0.4\n",
- " )\n",
- "\n",
- "fig.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pNgGeWRF-eyI"
- },
- "source": [
- "## Conclusion.\n",
- "\n",
- "In this tutorial, we explored how to perform multilingual text clustering using the MASSIVE dataset. We loaded the text data, applied K-Means clustering, and evaluated our clusters using differnet clustering performance evaluation metrics. The visualization and analysis helped us understand the efficacy of LASER across different languages."
- ]
- }
- ],
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- "accelerator": "GPU",
- "colab": {
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+ "source": [
+ "## Evaluating Clustering Performance\n",
+ "To evaluate our clustering, we'll use metrics suited for clustering quality, such as the Adjusted Rand Index (ARI), Normalized Mutual Information Score and Silhouette Coefficient:\n",
+ "\n"
],
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+ "source": [
+ "from sklearn.metrics import rand_score,adjusted_rand_score, normalized_mutual_info_score, silhouette_score\n",
+ "\n",
+ "labels = list(range(20)) * len(data_dict)\n",
+ "\n",
+ "ri_score = rand_score(labels, clusters)\n",
+ "ari_score = adjusted_rand_score(labels, clusters)\n",
+ "nmi_score = normalized_mutual_info_score(labels, clusters, average_method='arithmetic')\n",
+ "silhouette_avg = silhouette_score(embeddings, clusters)\n",
+ "\n",
+ "print(f\"Rand Index: {ri_score}\")\n",
+ "print(f\"Adjusted Rand Index: {ari_score}\")\n",
+ "print(f\"Normalized Mutual Information: {nmi_score}\")\n",
+ "print(f\"Silhouette Coefficient: {silhouette_avg}\")\n"
],
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+ "colab": {
+ "base_uri": "https://localhost:8080/"
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+ "outputId": "3ed3c520-9cae-4baa-fcbf-623236cfa976"
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+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Rand Index: 0.9731155778894472\n",
+ "Adjusted Rand Index: 0.7050871871580726\n",
+ "Normalized Mutual Information: 0.862654325567496\n",
+ "Silhouette Coefficient: 0.22755876183509827\n"
+ ]
+ }
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+ "## Clustering Evaluation Summary with LASER Embeddings\n",
+ "\n",
+ "We evaluated our clustering model, which leverages LASER embeddings, with the following results:\n",
+ "\n",
+ "- **Rand Index (RI):** 0.973 - High accuracy, indicating successful clustering.\n",
+ "- **Adjusted Rand Index (ARI):** 0.705 - Shows good performance, considering the adjustment for random chance.\n",
+ "- **Normalized Mutual Information (NMI):** 0.862 - Demonstrates strong alignment between our clustering and the true labels.\n",
+ "- **Silhouette Coefficient:** 0.227 - Suggests moderate cluster separation.\n",
+ "\n",
+ "### Role of LASER in Clustering Performance\n",
+ "\n",
+ "The effectiveness of our clustering, as evidenced by the high RI, ARI, and NMI scores, can be significantly attributed to the LASER embeddings. LASER's ability to create language-agnostic sentence representations has likely enhanced the clustering quality, enabling the algorithm to group sentences based on semantic similarity across different languages. This is further supported by the reasonable Silhouette Coefficient, indicating decent separation between clusters. These results showcase LASER’s capability in handling multilingual data.\n"
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+ "## Prepare Data for Plotting\n",
+ "We organize our data for easy ploting using `plotly`:"
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+ "languages = []\n",
+ "for lang, sentence in data_dict.items():\n",
+ " languages.extend([lang] * len(sentence))\n",
+ "\n",
+ "# Create a DataFrame for plotting\n",
+ "df = pd.DataFrame({\n",
+ " 'TSNE Component 1': reduced_embeddings[:, 0],\n",
+ " 'TSNE Component 2': reduced_embeddings[:, 1],\n",
+ " 'Language': languages,\n",
+ " 'Cluster': ['Cluster {}'.format(cluster) for cluster in clusters],\n",
+ " 'Sentence': combined_sentences\n",
+ "})\n"
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"
+ },
+ "metadata": {}
+ }
+ ]
},
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- "_model_module": "@jupyter-widgets/controls",
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- "IPY_MODEL_ee2ba3e0660941879944c3de796f7974",
- "IPY_MODEL_f2f2eb28e4f840319b6c54fee6d8fe84"
+ {
+ "cell_type": "code",
+ "source": [
+ "num_languages = len(data_dict) # Number of languages\n",
+ "num_sentences = len(reduced_embeddings) // num_languages\n",
+ "\n",
+ "cluster_centers = np.array([np.mean(reduced_embeddings[i::num_sentences], axis=0) for i in range(num_sentences)])"
],
- "layout": "IPY_MODEL_8989be675546467f8f8b3e379174da71"
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+ " hover_data=['Sentence'])\n",
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+ " mode='markers',\n",
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