From a934ac70f62af4a326f22d85c538653be8da81ad Mon Sep 17 00:00:00 2001
From: paul <okewunmipaul@yahoo.com>
Date: Fri, 1 Dec 2023 07:55:17 +0100
Subject: [PATCH 1/7] Add laser clustering example notebook

---
 tasks/clustering/LaserClusteringExample.ipynb | 6107 +++++++++++++++++
 tasks/clustering/README.md                    |   13 +
 2 files changed, 6120 insertions(+)
 create mode 100644 tasks/clustering/LaserClusteringExample.ipynb
 create mode 100644 tasks/clustering/README.md

diff --git a/tasks/clustering/LaserClusteringExample.ipynb b/tasks/clustering/LaserClusteringExample.ipynb
new file mode 100644
index 00000000..df74c6d2
--- /dev/null
+++ b/tasks/clustering/LaserClusteringExample.ipynb
@@ -0,0 +1,6107 @@
+{
+ "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": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
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+    "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"
+    },
+    {
+     "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"
+    },
+    {
+     "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"
+    },
+    {
+     "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"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "735958954b1e4d93a9560c12445d82fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4e4f7bf09f24b16a6fad76b59c8c9eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a9f4a172be042639fc58f493e6d3cbb",
+       "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": "48284f06ee714f53b2d87a51afe9bfc8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "449acb6cf785447dbd524da69a01f19d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be3be7c622f94d2c8dd64451667c8677",
+       "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": "b19577b5a17349f581fe83216a9f0b87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a0a502d2f0e4879970cffd05ec6444f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "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"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aab860a2027d4418adf00f01d1648a6f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "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/"
+    },
+    "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
+    },
+    "id": "0JG_MA7-lXMW",
+    "outputId": "e45f02e8-3fa0-4b5f-ecb6-36b06dd998b0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<html>\n",
+       "<head><meta charset=\"utf-8\" /></head>\n",
+       "<body>\n",
+       "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"ddac4148-24d3-47d0-aa4e-10165b33d0b7\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"ddac4148-24d3-47d0-aa4e-10165b33d0b7\")) {                    Plotly.newPlot(                        \"ddac4148-24d3-47d0-aa4e-10165b33d0b7\",                        [{\"customdata\":[[\"wake me up at nine am on friday\"],[\"set an alarm for two hours from now\"],[\"olly quiet\"],[\"stop\"],[\"olly pause for ten seconds\"],[\"pause for ten seconds\"],[\"make the lighting bit more warm here\"],[\"please set the lighting suitable for reading\"],[\"time to sleep\"],[\"time to sleep olly\"],[\"turn off the light in the bathroom\"],[\"olly dim the lights in the hall\"],[\"turn 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+       "                            \n",
+       "var gd = document.getElementById('ddac4148-24d3-47d0-aa4e-10165b33d0b7');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
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+       "\n",
+       "                        })                };                            </script>        </div>\n",
+       "</body>\n",
+       "</html>"
+      ]
+     },
+     "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
+    },
+    "id": "mkpQlPP4unaU",
+    "outputId": "3610200a-1e26-4da2-9879-e6a53a4dc96a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<html>\n",
+       "<head><meta charset=\"utf-8\" /></head>\n",
+       "<body>\n",
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+       "                            \n",
+       "var gd = document.getElementById('c5812407-55f8-4620-987f-9c69488d3061');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                            </script>        </div>\n",
+       "</body>\n",
+       "</html>"
+      ]
+     },
+     "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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diff --git a/tasks/clustering/README.md b/tasks/clustering/README.md
new file mode 100644
index 00000000..23b7380c
--- /dev/null
+++ b/tasks/clustering/README.md
@@ -0,0 +1,13 @@
+# Laser Encoder: Sentiment Analysis
+
+## Overview
+
+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 was 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.
+
+## Getting Started
+
+To run the notebook in Google Colab, simply click the "Open in Colab" button below:
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Paulooh007/LASER-fork/blob/laser-clustering/tasks/clustering/LaserClusteringExample.ipynb)
+
+

From 3fb8e2e64282988ff75b20683b6db26a59691330 Mon Sep 17 00:00:00 2001
From: paul <okewunmipaul@yahoo.com>
Date: Fri, 1 Dec 2023 08:02:41 +0100
Subject: [PATCH 2/7] Fix public notebook url

---
 tasks/clustering/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tasks/clustering/README.md b/tasks/clustering/README.md
index 23b7380c..6ef6ec8f 100644
--- a/tasks/clustering/README.md
+++ b/tasks/clustering/README.md
@@ -8,6 +8,6 @@ In this tutorial, we'll explore the power of Language-Agnostic SEntence Represen
 
 To run the notebook in Google Colab, simply click the "Open in Colab" button below:
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Paulooh007/LASER-fork/blob/laser-clustering/tasks/clustering/LaserClusteringExample.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Paulooh007/LASER/blob/laser-clustering/tasks/clustering/LaserClusteringExample.ipynb)
 
 

From bd1f5f0e6c12bffbd9afaf1d7c777f3a2039615c Mon Sep 17 00:00:00 2001
From: paul <okewunmipaul@yahoo.com>
Date: Fri, 1 Dec 2023 11:16:13 +0100
Subject: [PATCH 3/7] Fix notebook rendering on github

---
 tasks/clustering/LaserClusteringExample.ipynb | 6493 ++---------------
 1 file changed, 463 insertions(+), 6030 deletions(-)

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": {
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-      "e0c963585ccf49fba9a25ea07b3b2018",
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-      "c83c646913b340d287933f26b1f212d5",
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-      "f2f2eb28e4f840319b6c54fee6d8fe84",
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-      "1898af2888a346479209d4565d6432b5"
-     ]
+      "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": [
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-   "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",
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-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "id": "FbKkpx5Eue0o"
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-   "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"
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-   "outputs": [
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-    "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()"
-   ]
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-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pNgGeWRF-eyI"
-   },
-   "source": [
-    "## Conclusion.\n",
-    "\n",
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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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-       "IPY_MODEL_7c7a6d5d407641ba84ec9e7953832055"
+    {
+      "cell_type": "code",
+      "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/"
+        },
+        "id": "w7zgwToT2N9N",
+        "outputId": "3ed3c520-9cae-4baa-fcbf-623236cfa976"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "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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+    {
+      "cell_type": "markdown",
+      "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"
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+        "We organize our data for easy ploting using `plotly`:"
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+        "\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",
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"
+          },
+          "metadata": {}
+        }
+      ]
     },
-    "aab860a2027d4418adf00f01d1648a6f": {
-     "model_module": "@jupyter-widgets/controls",
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-      "_dom_classes": [],
-      "_model_module": "@jupyter-widgets/controls",
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-      "_model_name": "HBoxModel",
-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/controls",
-      "_view_module_version": "1.5.0",
-      "_view_name": "HBoxView",
-      "box_style": "",
-      "children": [
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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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-      "height": null,
-      "justify_content": null,
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-      "left": null,
-      "margin": null,
-      "max_height": null,
-      "max_width": null,
-      "min_height": null,
-      "min_width": null,
-      "object_fit": null,
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-      "order": null,
-      "overflow": null,
-      "overflow_x": null,
-      "overflow_y": null,
-      "padding": null,
-      "right": null,
-      "top": null,
-      "visibility": null,
-      "width": null
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-      "_view_count": null,
-      "_view_module": "@jupyter-widgets/base",
-      "_view_module_version": "1.2.0",
-      "_view_name": "LayoutView",
-      "align_content": null,
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+        "# 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",
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+        "                 )\n",
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From 2b9ac6b8a9aa01b2e7263720e24677b09d127f15 Mon Sep 17 00:00:00 2001
From: paul <okewunmipaul@yahoo.com>
Date: Mon, 4 Dec 2023 10:19:18 +0100
Subject: [PATCH 4/7] Update clustering notebook

---
 tasks/clustering/LaserClusteringExample.ipynb | 12556 +++++++++++++++-
 1 file changed, 12464 insertions(+), 92 deletions(-)

diff --git a/tasks/clustering/LaserClusteringExample.ipynb b/tasks/clustering/LaserClusteringExample.ipynb
index 65256ab2..6bcd4c43 100644
--- a/tasks/clustering/LaserClusteringExample.ipynb
+++ b/tasks/clustering/LaserClusteringExample.ipynb
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@@ -48,10 +11338,40 @@
         "!pip install -q laser_encoders datasets"
       ],
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+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/521.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.4/521.2 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.8/521.2 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/895.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m890.9/895.1 kB\u001b[0m \u001b[31m31.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m895.1/895.1 kB\u001b[0m \u001b[31m23.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.6/9.6 MB\u001b[0m \u001b[31m78.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m279.9/279.9 kB\u001b[0m \u001b[31m35.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.4/112.4 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for fairseq (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for unicategories (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
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+      ]
     },
     {
       "cell_type": "code",
@@ -60,81 +11380,922 @@
       ],
       "metadata": {
         "id": "j4OXMzQvzZtb",
-        "outputId": "0c3b6473-a59d-4e74-bf25-64cf45e8d3f1",
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         "colab": {
           "base_uri": "https://localhost:8080/"
         }
       },
-      "execution_count": null,
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting kaleido\n",
+            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: kaleido\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "lida 0.0.10 requires fastapi, which is not installed.\n",
+            "lida 0.0.10 requires python-multipart, which is not installed.\n",
+            "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed kaleido-0.2.1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "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",
+        "\n",
+        "warnings.filterwarnings(\"ignore\")\n"
+      ]
+    },
+    {
+      "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"
+      }
+    },
+    {
+      "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",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
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+      },
+      "execution_count": 4,
       "outputs": [
         {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: kaleido in /usr/local/lib/python3.10/dist-packages (0.2.1)\n"
-          ]
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script:   0%|          | 0.00/30.3k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading readme:   0%|          | 0.00/34.4k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading data:   0%|          | 0.00/40.3M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c8fde077cf99461b9bdcff7a0f963ea1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5856c340fdb640d4a788db7a4f8b4b3c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "82950291031b41599f567d8534bb1400"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ce0301399faf4a7898d21e65bc748733"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
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+            }
+          },
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
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+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+            }
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
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+              "Generating test split: 0 examples [00:00, ? examples/s]"
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+              "version_major": 2,
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+              "Generating train split: 0 examples [00:00, ? examples/s]"
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4a92dbbf928646dbbc03753d079e0b19"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+              "model_id": "da867f9f7e7a47feb443338e24e003f0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+              "model_id": "aabefeeb237d4815a3ea0fda4b4a3656"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2b5b8de1d9ba4eb8bc380b38f782dcf6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f2dc6eaab3e0448cbab6f2d1afd8dab1"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating validation split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ba6b439456974ba3b1639f2750eb5ba7"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split: 0 examples [00:00, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "43ebd2dae677428cbb19b3f132c0886c"
+            }
+          },
+          "metadata": {}
         }
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "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",
-        "\n",
-        "warnings.filterwarnings(\"ignore\")\n"
-      ]
-    },
-    {
-      "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"
-      }
-    },
-    {
-      "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"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
     {
       "cell_type": "code",
       "source": [
@@ -154,7 +12315,7 @@
       "metadata": {
         "id": "sn-hexAdkkne"
       },
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": []
     },
     {
@@ -177,9 +12338,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "nwjzEPUqk4-9",
-        "outputId": "3b04501d-c642-4fa8-b746-1d607c390bb8"
+        "outputId": "0d30eb56-cccd-4525-ae9b-9641a53a44bf"
       },
-      "execution_count": null,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "stream",
@@ -225,9 +12386,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "_skE8eHnkpXV",
-        "outputId": "c3260a3a-21ab-46aa-9f78-91d08649ceb6"
+        "outputId": "6676a615-49e7-4428-9f93-d57ad0c0d07d"
       },
-      "execution_count": null,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "stream",
@@ -254,10 +12415,24 @@
         "embeddings = encoder.encode_sentences(combined_sentences, normalize_embeddings=True)"
       ],
       "metadata": {
-        "id": "pgWrd9KIEHIw"
+        "id": "pgWrd9KIEHIw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2ab966c1-88a5-4bdf-b542-7d79bf5237a8"
       },
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 1.01M/1.01M [00:00<00:00, 17.3MB/s]\n",
+            "100%|██████████| 179M/179M [00:02<00:00, 81.2MB/s]\n",
+            "100%|██████████| 470k/470k [00:00<00:00, 10.0MB/s]\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -282,7 +12457,7 @@
       "metadata": {
         "id": "kYvltGE_qpXy"
       },
-      "execution_count": null,
+      "execution_count": 9,
       "outputs": []
     },
     {
@@ -306,7 +12481,23 @@
       "metadata": {
         "id": "iYoDifv0lMNl"
       },
-      "execution_count": null,
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Group sentences by their cluster\n",
+        "clustered_sentences = {}\n",
+        "for sentence, cluster_label in zip(combined_sentences, clusters):\n",
+        "    if cluster_label not in clustered_sentences:\n",
+        "        clustered_sentences[cluster_label] = []\n",
+        "    clustered_sentences[cluster_label].append(sentence)\n"
+      ],
+      "metadata": {
+        "id": "vMS07RohFWnj"
+      },
+      "execution_count": 11,
       "outputs": []
     },
     {
@@ -314,12 +12505,46 @@
       "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"
+        "\n",
+        "We can also observe below that the same sentences which were previously displayed in Cell 6, when we initially inspected the dataset, were correctly clustered together\n"
       ],
       "metadata": {
         "id": "QyqNtkxl2D8o"
       }
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "for sentence in clustered_sentences[7]:\n",
+        "    print(sentence)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-tkDQp4YFSpD",
+        "outputId": "52f76d3e-f95c-4ce2-e16e-3411ef920e7d"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "wake me up at nine am on friday\n",
+            "réveille-moi à neuf heures du matin le vendredi\n",
+            "svegliami alle nove di mattina venerdì\n",
+            "despiértame a las nueve de la mañana el viernes\n",
+            "金曜日の午前九時に起こしてください\n",
+            "صحيني تسعة الصباح يوم الجمعة\n",
+            "星期五早上九点叫醒我\n",
+            "maak my wakker nege-uur v. m. op vrydag\n",
+            "разбуди меня в девять утра в пятницу\n",
+            "शुक्रवार को सुबह नौ बजे मुझे जगा दो\n"
+          ]
+        }
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
@@ -342,9 +12567,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "w7zgwToT2N9N",
-        "outputId": "3ed3c520-9cae-4baa-fcbf-623236cfa976"
+        "outputId": "4c874a19-591a-4906-f70a-97c12f458145"
       },
-      "execution_count": null,
+      "execution_count": 13,
       "outputs": [
         {
           "output_type": "stream",
@@ -407,7 +12632,7 @@
       "metadata": {
         "id": "VVft-ZUMEtPP"
       },
-      "execution_count": null,
+      "execution_count": 14,
       "outputs": []
     },
     {
@@ -429,7 +12654,7 @@
       "metadata": {
         "id": "6vu1UTQVmLu9"
       },
-      "execution_count": null,
+      "execution_count": 15,
       "outputs": []
     },
     {
@@ -448,14 +12673,14 @@
           "height": 517
         },
         "id": "0JG_MA7-lXMW",
-        "outputId": "525b304f-37b7-4250-a743-7b5c42a283eb"
+        "outputId": "bc556d12-39d5-4b64-fb1e-d73bba7f5894"
       },
-      "execution_count": null,
+      "execution_count": 16,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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"
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"
           },
           "metadata": {}
         }
@@ -472,7 +12697,7 @@
       "metadata": {
         "id": "FbKkpx5Eue0o"
       },
-      "execution_count": null,
+      "execution_count": 17,
       "outputs": []
     },
     {
@@ -512,14 +12737,161 @@
           "height": 517
         },
         "id": "mkpQlPP4unaU",
-        "outputId": "05b1d00c-7560-42a0-d42a-24a1847c51eb"
+        "outputId": "53c66cee-c70b-492e-9f1d-bf8f582069b8"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Plot Randomly Selected Clusters.\n",
+        "\n",
+        "To closely inspect the sentences within each cluster, let's randomly display different clusters and examine their proximity in the embedding space."
+      ],
+      "metadata": {
+        "id": "zoPiOP49Fk58"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import random"
+      ],
+      "metadata": {
+        "id": "s9JqzeO5HCBK"
+      },
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "random_cluster = random.choice(range(n_clusters))\n",
+        "print(f\"Sentences in cluster {random_cluster}:\")\n",
+        "for sentence in clustered_sentences[random_cluster]:\n",
+        "    print(sentence)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lMzYKIMfFl4s",
+        "outputId": "ab3fe9b9-c226-4cee-f796-5898835a4b0f"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Sentences in cluster 2:\n",
+            "time to sleep\n",
+            "l'heure de dormir\n",
+            "è ora di andare a letto\n",
+            "hora de dormir\n",
+            "寝る時間\n",
+            "وقت النوم\n",
+            "是时候睡觉了\n",
+            "tyd om te slaap\n",
+            "tyd om te slaap janneman\n",
+            "время идти спать\n",
+            "олли время спать\n",
+            "सोने का समय\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Prepare data for the selected cluster\n",
+        "selected_indices = [i for i, label in enumerate(clusters) if label == random_cluster]\n",
+        "selected_embeddings = reduced_embeddings[selected_indices]\n",
+        "selected_sentences = [combined_sentences[i] for i in selected_indices]\n",
+        "selected_sentences_languages =  [languages[i] for i in selected_indices]\n",
+        "\n",
+        "df_selected = pd.DataFrame({\n",
+        "    'Component 1': selected_embeddings[:, 0],\n",
+        "    'Component 2': selected_embeddings[:, 1],\n",
+        "    'Sentence': selected_sentences,\n",
+        "    \"Language\": selected_sentences_languages\n",
+        "})\n",
+        "\n",
+        "fig = px.scatter(df_selected, x='Component 1', y='Component 2',color='Language', text='Sentence', title=f'Sentences in Cluster {random_cluster}')\n",
+        "fig.update_traces(textposition='top center')\n",
+        "fig.update_layout(showlegend=True)\n",
+        "fig.show(\"png\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517
+        },
+        "id": "3_gTVXAJFsG9",
+        "outputId": "26bb54c7-9486-4635-8599-0235ed551281"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### The cluster relative to the whole embeddings space."
+      ],
+      "metadata": {
+        "id": "aYio2usqFxyD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_all = pd.DataFrame({\n",
+        "    'Component 1': reduced_embeddings[:, 0],\n",
+        "    'Component 2': reduced_embeddings[:, 1],\n",
+        "    'Language': languages\n",
+        "})\n",
+        "\n",
+        "# Plot the entire dataset\n",
+        "fig = px.scatter(df_all, x='Component 1', y='Component 2', color='Language', opacity=0.3)\n",
+        "\n",
+        "# Overlay the selected cluster\n",
+        "fig.add_scatter(x=df_selected['Component 1'], y=df_selected['Component 2'], mode='markers', text=df_selected['Sentence'], marker=dict(color='green'), textposition='top center')\n",
+        "\n",
+        "# Update layout\n",
+        "fig.update_layout(title=f'Sentences in Cluster {random_cluster}', showlegend=True)\n",
+        "fig.show(\"png\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 517
+        },
+        "id": "knwAXi4EFwx_",
+        "outputId": "1e23469d-1757-4835-9bef-42e49dcb22d8"
       },
-      "execution_count": null,
+      "execution_count": 25,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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"
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"
           },
           "metadata": {}
         }

From a511791c9afd432340465719cd1d7c3fc1042e3f Mon Sep 17 00:00:00 2001
From: paul <okewunmipaul@yahoo.com>
Date: Tue, 5 Dec 2023 16:17:21 +0100
Subject: [PATCH 5/7] Update markdown cell in notebook

---
 tasks/clustering/LaserClusteringExample.ipynb | 48 +++++++++----------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/tasks/clustering/LaserClusteringExample.ipynb b/tasks/clustering/LaserClusteringExample.ipynb
index 6bcd4c43..0bfe5249 100644
--- a/tasks/clustering/LaserClusteringExample.ipynb
+++ b/tasks/clustering/LaserClusteringExample.ipynb
@@ -11311,7 +11311,7 @@
       "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"
+        "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 semantic content or meaning 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"
@@ -11344,7 +11344,7 @@
         },
         "outputId": "41b40ab7-a601-407e-cbca-18ce1dc4137e"
       },
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -11385,7 +11385,7 @@
           "base_uri": "https://localhost:8080/"
         }
       },
-      "execution_count": 2,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -11406,7 +11406,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": null,
       "metadata": {
         "id": "vWPUBx6u6A2u"
       },
@@ -11830,7 +11830,7 @@
         },
         "outputId": "5848fa25-fe82-4bbf-acdd-e12d4c069d3c"
       },
-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -12315,7 +12315,7 @@
       "metadata": {
         "id": "sn-hexAdkkne"
       },
-      "execution_count": 5,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -12340,7 +12340,7 @@
         "id": "nwjzEPUqk4-9",
         "outputId": "0d30eb56-cccd-4525-ae9b-9641a53a44bf"
       },
-      "execution_count": 6,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -12388,7 +12388,7 @@
         "id": "_skE8eHnkpXV",
         "outputId": "6676a615-49e7-4428-9f93-d57ad0c0d07d"
       },
-      "execution_count": 7,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -12421,7 +12421,7 @@
         },
         "outputId": "2ab966c1-88a5-4bdf-b542-7d79bf5237a8"
       },
-      "execution_count": 8,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -12441,7 +12441,7 @@
         "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."
+        "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 semantic 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"
@@ -12457,7 +12457,7 @@
       "metadata": {
         "id": "kYvltGE_qpXy"
       },
-      "execution_count": 9,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -12481,7 +12481,7 @@
       "metadata": {
         "id": "iYoDifv0lMNl"
       },
-      "execution_count": 10,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -12497,7 +12497,7 @@
       "metadata": {
         "id": "vMS07RohFWnj"
       },
-      "execution_count": 11,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -12525,7 +12525,7 @@
         "id": "-tkDQp4YFSpD",
         "outputId": "52f76d3e-f95c-4ce2-e16e-3411ef920e7d"
       },
-      "execution_count": 12,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -12569,7 +12569,7 @@
         "id": "w7zgwToT2N9N",
         "outputId": "4c874a19-591a-4906-f70a-97c12f458145"
       },
-      "execution_count": 13,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -12632,7 +12632,7 @@
       "metadata": {
         "id": "VVft-ZUMEtPP"
       },
-      "execution_count": 14,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -12654,7 +12654,7 @@
       "metadata": {
         "id": "6vu1UTQVmLu9"
       },
-      "execution_count": 15,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -12675,7 +12675,7 @@
         "id": "0JG_MA7-lXMW",
         "outputId": "bc556d12-39d5-4b64-fb1e-d73bba7f5894"
       },
-      "execution_count": 16,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -12697,7 +12697,7 @@
       "metadata": {
         "id": "FbKkpx5Eue0o"
       },
-      "execution_count": 17,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -12739,7 +12739,7 @@
         "id": "mkpQlPP4unaU",
         "outputId": "53c66cee-c70b-492e-9f1d-bf8f582069b8"
       },
-      "execution_count": 18,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "display_data",
@@ -12769,7 +12769,7 @@
       "metadata": {
         "id": "s9JqzeO5HCBK"
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From e2898179c514e80cff563f94b7e656fc3fd4ef53 Mon Sep 17 00:00:00 2001
From: paul <okewunmipaul@yahoo.com>
Date: Thu, 7 Dec 2023 20:03:18 +0100
Subject: [PATCH 6/7] Update some visualization errors and REAME

---
 tasks/clustering/LaserClusteringExample.ipynb | 12262 +---------------
 tasks/clustering/README.md                    |     6 +
 2 files changed, 50 insertions(+), 12218 deletions(-)

diff --git a/tasks/clustering/LaserClusteringExample.ipynb b/tasks/clustering/LaserClusteringExample.ipynb
index 0bfe5249..5fa7ef0a 100644
--- a/tasks/clustering/LaserClusteringExample.ipynb
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@@ -11338,40 +48,10 @@
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-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.4/112.4 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for fairseq (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for unicategories (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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@@ -11380,33 +60,25 @@
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         "colab": {
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-      "execution_count": null,
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-            "Collecting kaleido\n",
-            "  Downloading kaleido-0.2.1-py2.py3-none-manylinux1_x86_64.whl (79.9 MB)\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.9/79.9 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hInstalling collected packages: kaleido\n",
-            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
-            "lida 0.0.10 requires fastapi, which is not installed.\n",
-            "lida 0.0.10 requires python-multipart, which is not installed.\n",
-            "lida 0.0.10 requires uvicorn, which is not installed.\u001b[0m\u001b[31m\n",
-            "\u001b[0mSuccessfully installed kaleido-0.2.1\n"
+            "Requirement already satisfied: kaleido in /usr/local/lib/python3.10/dist-packages (0.2.1)\n"
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       },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading builder script:   0%|          | 0.00/30.3k [00:00<?, ?B/s]"
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-          "data": {
-            "text/plain": [
-              "Downloading readme:   0%|          | 0.00/34.4k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
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-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Downloading data:   0%|          | 0.00/40.3M [00:00<?, ?B/s]"
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-          "data": {
-            "text/plain": [
-              "Generating train split: 0 examples [00:00, ? examples/s]"
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-              "Generating test split: 0 examples [00:00, ? examples/s]"
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-            }
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-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating train split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "f2dc6eaab3e0448cbab6f2d1afd8dab1"
-            }
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-          "metadata": {}
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-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating validation split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "ba6b439456974ba3b1639f2750eb5ba7"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating test split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "43ebd2dae677428cbb19b3f132c0886c"
-            }
-          },
-          "metadata": {}
-        }
-      ]
+      "execution_count": 4,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -12315,7 +154,7 @@
       "metadata": {
         "id": "sn-hexAdkkne"
       },
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": []
     },
     {
@@ -12338,9 +177,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "nwjzEPUqk4-9",
-        "outputId": "0d30eb56-cccd-4525-ae9b-9641a53a44bf"
+        "outputId": "ab5392f4-79a2-453b-8753-dfbf5089d53a"
       },
-      "execution_count": null,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "stream",
@@ -12386,9 +225,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "_skE8eHnkpXV",
-        "outputId": "6676a615-49e7-4428-9f93-d57ad0c0d07d"
+        "outputId": "6472326c-0d1b-48f9-aa67-cba3c41ced9f"
       },
-      "execution_count": null,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "stream",
@@ -12415,24 +254,10 @@
         "embeddings = encoder.encode_sentences(combined_sentences, normalize_embeddings=True)"
       ],
       "metadata": {
-        "id": "pgWrd9KIEHIw",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "2ab966c1-88a5-4bdf-b542-7d79bf5237a8"
+        "id": "pgWrd9KIEHIw"
       },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "100%|██████████| 1.01M/1.01M [00:00<00:00, 17.3MB/s]\n",
-            "100%|██████████| 179M/179M [00:02<00:00, 81.2MB/s]\n",
-            "100%|██████████| 470k/470k [00:00<00:00, 10.0MB/s]\n"
-          ]
-        }
-      ]
+      "execution_count": 8,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -12457,7 +282,7 @@
       "metadata": {
         "id": "kYvltGE_qpXy"
       },
-      "execution_count": null,
+      "execution_count": 9,
       "outputs": []
     },
     {
@@ -12481,7 +306,7 @@
       "metadata": {
         "id": "iYoDifv0lMNl"
       },
-      "execution_count": null,
+      "execution_count": 10,
       "outputs": []
     },
     {
@@ -12497,7 +322,7 @@
       "metadata": {
         "id": "vMS07RohFWnj"
       },
-      "execution_count": null,
+      "execution_count": 11,
       "outputs": []
     },
     {
@@ -12523,9 +348,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "-tkDQp4YFSpD",
-        "outputId": "52f76d3e-f95c-4ce2-e16e-3411ef920e7d"
+        "outputId": "b218cbcc-48a6-47e8-c5b8-a831a4cdbab4"
       },
-      "execution_count": null,
+      "execution_count": 12,
       "outputs": [
         {
           "output_type": "stream",
@@ -12567,9 +392,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "w7zgwToT2N9N",
-        "outputId": "4c874a19-591a-4906-f70a-97c12f458145"
+        "outputId": "2fa3d8a4-6cac-4bfc-d55d-29e438914dce"
       },
-      "execution_count": null,
+      "execution_count": 13,
       "outputs": [
         {
           "output_type": "stream",
@@ -12632,7 +457,7 @@
       "metadata": {
         "id": "VVft-ZUMEtPP"
       },
-      "execution_count": null,
+      "execution_count": 14,
       "outputs": []
     },
     {
@@ -12654,7 +479,7 @@
       "metadata": {
         "id": "6vu1UTQVmLu9"
       },
-      "execution_count": null,
+      "execution_count": 15,
       "outputs": []
     },
     {
@@ -12673,9 +498,9 @@
           "height": 517
         },
         "id": "0JG_MA7-lXMW",
-        "outputId": "bc556d12-39d5-4b64-fb1e-d73bba7f5894"
+        "outputId": "46127dc5-49ea-4acb-d6c7-6218a9e2cd2b"
       },
-      "execution_count": null,
+      "execution_count": 16,
       "outputs": [
         {
           "output_type": "display_data",
@@ -12697,7 +522,7 @@
       "metadata": {
         "id": "FbKkpx5Eue0o"
       },
-      "execution_count": null,
+      "execution_count": 17,
       "outputs": []
     },
     {
@@ -12737,9 +562,9 @@
           "height": 517
         },
         "id": "mkpQlPP4unaU",
-        "outputId": "53c66cee-c70b-492e-9f1d-bf8f582069b8"
+        "outputId": "2ac37329-7dfc-4657-ff01-508c38d514b1"
       },
-      "execution_count": null,
+      "execution_count": 18,
       "outputs": [
         {
           "output_type": "display_data",
@@ -12769,7 +594,7 @@
       "metadata": {
         "id": "s9JqzeO5HCBK"
       },
-      "execution_count": null,
+      "execution_count": 19,
       "outputs": []
     },
     {
@@ -12785,9 +610,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "lMzYKIMfFl4s",
-        "outputId": "ab3fe9b9-c226-4cee-f796-5898835a4b0f"
+        "outputId": "579d4c7a-25dd-4fe4-80ad-926aa690df91"
       },
-      "execution_count": null,
+      "execution_count": 20,
       "outputs": [
         {
           "output_type": "stream",
@@ -12837,9 +662,9 @@
           "height": 517
         },
         "id": "3_gTVXAJFsG9",
-        "outputId": "26bb54c7-9486-4635-8599-0235ed551281"
+        "outputId": "8c95da2a-e4b0-484f-dff3-8a01a91b04cc"
       },
-      "execution_count": null,
+      "execution_count": 21,
       "outputs": [
         {
           "output_type": "display_data",
@@ -12872,10 +697,11 @@
         "fig = px.scatter(df_all, x='Component 1', y='Component 2', color='Language', opacity=0.3)\n",
         "\n",
         "# Overlay the selected cluster\n",
-        "fig.add_scatter(x=df_selected['Component 1'], y=df_selected['Component 2'], mode='markers', text=df_selected['Sentence'], marker=dict(color='green'), textposition='top center')\n",
+        "fig.add_scatter(x=df_selected['Component 1'], y=df_selected['Component 2'], mode='markers', text=df_selected['Sentence'],\n",
+        "                name='Selected Cluster', marker=dict(color='green'), textposition='top center')\n",
         "\n",
         "# Update layout\n",
-        "fig.update_layout(title=f'Sentences in Cluster {random_cluster}', showlegend=True)\n",
+        "fig.update_layout(title=f'Sentences in Cluster {random_cluster} (highlighted in green)', showlegend=True)\n",
         "fig.show(\"png\")\n"
       ],
       "metadata": {
@@ -12884,14 +710,14 @@
           "height": 517
         },
         "id": "knwAXi4EFwx_",
-        "outputId": "1e23469d-1757-4835-9bef-42e49dcb22d8"
+        "outputId": "995a1a20-0d73-4329-dd60-03cbf757408e"
       },
-      "execution_count": null,
+      "execution_count": 22,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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"
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"
           },
           "metadata": {}
         }
diff --git a/tasks/clustering/README.md b/tasks/clustering/README.md
index 6ef6ec8f..6bed6c28 100644
--- a/tasks/clustering/README.md
+++ b/tasks/clustering/README.md
@@ -10,4 +10,10 @@ To run the notebook in Google Colab, simply click the "Open in Colab" button bel
 
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Paulooh007/LASER/blob/laser-clustering/tasks/clustering/LaserClusteringExample.ipynb)
 
+## Simple interactive Demo
+To better understand this tutorial, you can checkout this interactive demo hosted on huggingface spaces.
+
+[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-lg.svg)](https://huggingface.co/spaces/paulokewunmi/laser_multilingual_embeddings_viz)
+
+
 

From 56dd36376bb65abb7fbf64b6e641116219ec5eb3 Mon Sep 17 00:00:00 2001
From: paul <okewunmipaul@yahoo.com>
Date: Thu, 7 Dec 2023 20:16:21 +0100
Subject: [PATCH 7/7] Update README

---
 tasks/clustering/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tasks/clustering/README.md b/tasks/clustering/README.md
index 6bed6c28..381a0853 100644
--- a/tasks/clustering/README.md
+++ b/tasks/clustering/README.md
@@ -2,7 +2,7 @@
 
 ## Overview
 
-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 was 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.
+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 was to show that LASER embeddings can effectively group texts not only by their semantic 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.
 
 ## Getting Started