{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Persian-WordVectors-FastText.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyNm/uxPHQjbjbEcWfKfSod9"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"3-NSJ-cnd4IH","colab_type":"text"},"source":["# FastText Embeddings for Persian Language\n","\n","* Trained on ``OSCAR`` dataset\n","\n","* Vector Size ``100``\n"]},{"cell_type":"markdown","metadata":{"id":"i_JpW-ZEeB4z","colab_type":"text"},"source":["### Installing FastText"]},{"cell_type":"code","metadata":{"id":"TLmzLrPydr6a","colab_type":"code","outputId":"6bda1ea3-ff80-470c-982c-25fdf976bd2f","executionInfo":{"status":"ok","timestamp":1588754395254,"user_tz":-270,"elapsed":3846,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["!git clone https://github.com/facebookresearch/fastText.git "],"execution_count":1,"outputs":[{"output_type":"stream","text":["Cloning into 'fastText'...\n","remote: Enumerating objects: 12, done.\u001b[K\n","remote: Counting objects: 100% (12/12), done.\u001b[K\n","remote: Compressing objects: 100% (10/10), done.\u001b[K\n","remote: Total 3826 (delta 1), reused 4 (delta 0), pack-reused 3814\u001b[K\n","Receiving objects: 100% (3826/3826), 8.21 MiB | 15.79 MiB/s, done.\n","Resolving deltas: 100% (2402/2402), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BTaw_vgtn2Dv","colab_type":"code","outputId":"c7a650fd-54cb-4d5e-8d02-31768ae75792","executionInfo":{"status":"ok","timestamp":1588754487099,"user_tz":-270,"elapsed":45670,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["!cd fastText && pip install ."],"execution_count":2,"outputs":[{"output_type":"stream","text":["Processing /content/fastText\n","Requirement already satisfied: pybind11>=2.2 in /usr/local/lib/python3.6/dist-packages (from fasttext==0.9.2) (2.5.0)\n","Requirement already satisfied: setuptools>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from fasttext==0.9.2) (46.1.3)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from fasttext==0.9.2) (1.18.3)\n","Building wheels for collected packages: fasttext\n","  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for fasttext: filename=fasttext-0.9.2-cp36-cp36m-linux_x86_64.whl size=3012853 sha256=b184dd3d088b50344e1df834fc35d1a072f25dd8cbcf72fb712633b2b85bac3f\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-52wqc9b3/wheels/a1/9f/52/696ce6c5c46325e840c76614ee5051458c0df10306987e7443\n","Successfully built fasttext\n","Installing collected packages: fasttext\n","Successfully installed fasttext-0.9.2\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"xxq-AfPgehFd","colab_type":"text"},"source":["## ⚠️ RESTART KERNEL\n","\n","Before Continuing, You have to restart the Runtime"]},{"cell_type":"markdown","metadata":{"id":"U_piOcUigU-I","colab_type":"text"},"source":["## Get The Model"]},{"cell_type":"code","metadata":{"id":"MjUX7f6Eel5O","colab_type":"code","colab":{}},"source":["SKIPGRAM_MODEL_FILE_ID = '1wPnMG9_GNUVdSgbznQziQc5nMWI3QKNz'\n","CBOW_MODEL_FILE_ID = '1cQP10CGV6kAwmRuESJ5RTsgHq5TveXwV'"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lDAEvGeAel93","colab_type":"code","outputId":"0a6709b6-a5a7-4ade-b42c-996437bd9a97","executionInfo":{"status":"ok","timestamp":1588754614419,"user_tz":-270,"elapsed":147633,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}},"colab":{"base_uri":"https://localhost:8080/","height":85}},"source":["# Let's explore the SKIPGRAM Model\n","\n","!gdown --id $SKIPGRAM_MODEL_FILE_ID "],"execution_count":4,"outputs":[{"output_type":"stream","text":["Downloading...\n","From: https://drive.google.com/uc?id=1wPnMG9_GNUVdSgbznQziQc5nMWI3QKNz\n","To: /content/farsi-dedup-skipgram.bin\n","4.37GB [00:52, 83.5MB/s]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"kR10-nSUgbLx","colab_type":"text"},"source":["## Load the Model"]},{"cell_type":"code","metadata":{"id":"JfXaALqjgTQv","colab_type":"code","outputId":"dcaaee7f-e2f9-49ae-ac2d-ed421edcae12","executionInfo":{"status":"ok","timestamp":1588760410676,"user_tz":-270,"elapsed":56581,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["import fasttext.util\n","# loading the Model\n","model_skipgram = fasttext.load_model('farsi-dedup-skipgram.bin')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"gCjG1DKurR-t","colab_type":"text"},"source":["# Getting Nearest Words, for word ``سگ``\n","``Dog`` in persian is ``سگ``\n","\n","\n","![alt text](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Lava_the_sled_dog.jpg/800px-Lava_the_sled_dog.jpg)\n","\n","Image taken from [here](https://commons.wikimedia.org/wiki/File:Lava_the_sled_dog.jpg)"]},{"cell_type":"code","metadata":{"id":"MAdg99T0gTS4","colab_type":"code","outputId":"a6d0f091-4c01-4b36-9458-237b56c2adda","executionInfo":{"status":"ok","timestamp":1588760451471,"user_tz":-270,"elapsed":40782,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}},"colab":{"base_uri":"https://localhost:8080/","height":442}},"source":["model_skipgram.get_nearest_neighbors('سگ', 25)"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[(0.782295823097229, 'روتوایلر'),\n"," (0.7789124250411987, 'توله'),\n"," (0.7602617144584656, 'شیتزو'),\n"," (0.758849024772644, 'گربه'),\n"," (0.7493144273757935, 'بولداگ'),\n"," (0.7487086653709412, 'پامرانیان'),\n"," (0.7453492283821106, 'چاوچاو'),\n"," (0.7400702834129333, 'تریر'),\n"," (0.7363772988319397, 'پامرانیا'),\n"," (0.7327418923377991, 'رتوایلر'),\n"," (0.7243485450744629, 'پاگ'),\n"," (0.7216327786445618, 'روتوایلر،'),\n"," (0.721341609954834, '_سگ'),\n"," (0.719440758228302, 'ایگوانا'),\n"," (0.7192081212997437, 'سگسگ'),\n"," (0.7191919088363647, '،سگ'),\n"," (0.7173099517822266, 'سگ،'),\n"," (0.7163112163543701, 'شپهرد'),\n"," (0.7139168977737427, 'سگ,سگ'),\n"," (0.7111517786979675, 'گربهء'),\n"," (0.7074176073074341, 'سگ\\u200cهای'),\n"," (0.7066739201545715, '.سگ'),\n"," (0.7048391699790955, 'دوبرمن'),\n"," (0.7031064033508301, 'روتوایلر...'),\n"," (0.7029660940170288, 'قدرجونی')]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"2_STAqZgrCLW","colab_type":"text"},"source":["Results are very interesting, for example, different kinds of dog breeds are very close to the word \"Dog\" itself.\n","\n","\n","\n","| Persian   \t| English    \t|\n","|-----------\t|------------\t|\n","| بولداگ    \t| Bulldog    \t|\n","| پاگ       \t| Pag        \t|\n","| پامرانیا  \t| Pamranya   \t|\n","| پامرانیان \t| Pamranyan  \t|\n","| تریر      \t| Terrier    \t|\n","| چاوچاو    \t| chạwchạw   \t|\n","| دوبرمن    \t| Doberman   \t|\n","| رتوایلر   \t| Rtvaylr    \t|\n","| روتوایلر  \t| Rottweiler \t|\n","| شپهرد     \t| Shepherd   \t|\n","| شیتزو     \t| Shih Tzu   \t|\n","| قدرجونی   \t| Qdrjvny    \t|\n","\n","\n","More interstingly, you can also see ``Cat`` (``گربه``)  and  ``Iguana ``  (``ایگوانا``) in the results "]},{"cell_type":"markdown","metadata":{"id":"QGHm38Q9ugMe","colab_type":"text"},"source":["## Do some plotting"]},{"cell_type":"code","metadata":{"id":"0JQsMNJDuojI","colab_type":"code","colab":{}},"source":["! pip install arabic_reshaper\n","! pip install python-bidi"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ldJ6Y1yyuh_I","colab_type":"code","colab":{}},"source":["import numpy as np\n","import matplotlib\n","from matplotlib import pyplot as plt\n","%matplotlib inline\n","\n","import seaborn as sns\n","\n","from bidi.algorithm import get_display\n","\n","import arabic_reshaper\n","\n","import pandas as pd"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hAw40WsLumhs","colab_type":"code","colab":{}},"source":["words =  [\n","'ایگوانا' , \n","'اسب',\n","'خر',\n","'الاغ',\n","'تمساح',\n","'شتر',\n","'گربه',\n","'پلنگ' ,\n","'روباه' ,\n","'موزخوار' ,\n","'گوزن' ,\n","'مرغ ' ,\n","'لاک‌پشت' ,\n","'شیر' ,\n","'زرافه',\n","'عقاب' ,\n","'بوفالو' ,\n","'ببر' ,\n","'غزال' ,\n","'وشق' ,\n","'گرگ' ,\n","'لک‌لک' ,\n","'کیوی' ,\n","'جگوار' ,\n","'گاو' ,\n","'دودو' ,\n","'کبک' ,\n","'شنقار' ,\n","'درازدامن' ,\n","'دلفین' ,\n","'خرس' ,\n","'صدف‌خوار' ,\n","'دجاج', \n","'بوقلمون', \n","'لانه‌کن', \n","'سمور', \n","'کرکس', \n","'توکان', \n","'تاکین', \n","'تاپیر بیرد', \n","'ویسنت', \n","'ماهی', \n","'شترمرغ', \n","'طاووس'\n","]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XgDeKE1fz9BQ","colab_type":"code","colab":{}},"source":["def plot_from_df(df, plot_title=\"\", label_distance=1):\n","  with sns.axes_style(\"whitegrid\"):\n","    ax = sns.lmplot('Component 1', # Horizontal axis\n","                    'Component 2', # Vertical axis\n","                    data=df, # Data source\n","                    fit_reg=False, # Don't fix a regression line\n","                    height = 7,\n","                    aspect =2 ) # size and dimension\n","\n","    plt.title(plot_title)\n","    plt.xlabel('Component 1')\n","    plt.ylabel('Component 2')\n","\n","    # Source: https://stackoverflow.com/questions/46027653/adding-labels-in-x-y-scatter-plot-with-seaborn\n","    def label_point(x, y, val, ax):\n","      a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)\n","      for i, point in a.iterrows():\n","        ax.text(point['x']+(label_distance*0.05), point['y']+(label_distance*0.05), get_display(arabic_reshaper.reshape(str.encode(point['val'], 'utf-8').decode())) )\n","\n","    label_point(df['Component 1'], df['Component 2'], df['Word'], plt.gca()) \n","\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BmG5y42fvCNK","colab_type":"code","colab":{}},"source":["# Get embedding for each word\n","embeddings = [model_skipgram[w] for w in words]"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"5i6csIWMzIcW","colab_type":"text"},"source":["# PCA"]},{"cell_type":"code","metadata":{"id":"o6j2cVs6vmBc","colab_type":"code","colab":{}},"source":["from sklearn.decomposition import PCA\n","from sklearn.preprocessing import StandardScaler"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vV5ovgz0umod","colab_type":"code","colab":{}},"source":["pca = PCA(n_components=2)\n","principalComponents = pca.fit_transform(embeddings)\n","\n","principalDf = pd.DataFrame( {'Word': pd.Series(words, dtype=str),\n","      'Component 1': pd.Series(principalComponents[:,0], dtype=float),\n","      'Component 2': pd.Series(principalComponents[:,1], dtype=float)})"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gYdb1RlTK2Q1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":529},"outputId":"da86b2fe-470a-46e2-deeb-d81bc57651ea","executionInfo":{"status":"ok","timestamp":1588760463240,"user_tz":-270,"elapsed":52499,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}}},"source":["plot_from_df(principalDf, \"PCA\")"],"execution_count":11,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x504 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"n6DVseCCzGQ4","colab_type":"text"},"source":["# t-SNE"]},{"cell_type":"code","metadata":{"id":"x4dVgdNfzMMI","colab_type":"code","colab":{}},"source":["from sklearn.manifold import TSNE"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"2HmiVSTsyRkN","colab_type":"code","outputId":"080006d1-6b6a-4be8-90fc-08f604d9d25d","executionInfo":{"status":"ok","timestamp":1588760463241,"user_tz":-270,"elapsed":52483,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"source":["tsne = TSNE(n_components=2, verbose=1, perplexity=5, n_iter=1000)\n","\n","tsne_results = tsne.fit_transform(embeddings)\n","tsneDf = pd.DataFrame( {'Word': pd.Series(words, dtype=str),\n","      'Component 1': pd.Series(tsne_results[:,0], dtype=float),\n","      'Component 2': pd.Series(tsne_results[:,1], dtype=float)})\n"],"execution_count":13,"outputs":[{"output_type":"stream","text":["[t-SNE] Computing 16 nearest neighbors...\n","[t-SNE] Indexed 44 samples in 0.002s...\n","[t-SNE] Computed neighbors for 44 samples in 0.002s...\n","[t-SNE] Computed conditional probabilities for sample 44 / 44\n","[t-SNE] Mean sigma: 1.080821\n","[t-SNE] KL divergence after 250 iterations with early exaggeration: 66.916046\n","[t-SNE] KL divergence after 1000 iterations: 0.876413\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MVpI5_8D0xxK","colab_type":"code","outputId":"d73fd27c-c95e-4430-e2d8-9eab09ff0ada","executionInfo":{"status":"ok","timestamp":1588760463241,"user_tz":-270,"elapsed":52476,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["tsneDf"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Word</th>\n","      <th>Component 1</th>\n","      <th>Component 2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ایگوانا</td>\n","      <td>-91.826584</td>\n","      <td>45.098480</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>اسب</td>\n","      <td>26.676863</td>\n","      <td>-132.220734</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>خر</td>\n","      <td>-0.695346</td>\n","      <td>-73.723679</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>الاغ</td>\n","      <td>-5.944066</td>\n","      <td>-119.264320</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>تمساح</td>\n","      <td>-19.752426</td>\n","      <td>46.634068</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>شتر</td>\n","      <td>16.000875</td>\n","      <td>-110.453720</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>گربه</td>\n","      <td>-76.900780</td>\n","      <td>48.546539</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>پلنگ</td>\n","      <td>20.580557</td>\n","      <td>114.023643</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>روباه</td>\n","      <td>-44.781902</td>\n","      <td>41.786091</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>موزخوار</td>\n","      <td>83.924042</td>\n","      <td>-29.970369</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>گوزن</td>\n","      <td>-1.669342</td>\n","      <td>51.262852</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>مرغ</td>\n","      <td>73.797386</td>\n","      <td>-118.267479</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>لاک‌پشت</td>\n","      <td>-73.737640</td>\n","      <td>107.210884</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>شیر</td>\n","      <td>104.045219</td>\n","      <td>-75.137169</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>زرافه</td>\n","      <td>-27.329851</td>\n","      <td>133.570709</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>عقاب</td>\n","      <td>-95.978836</td>\n","      <td>-18.559546</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>بوفالو</td>\n","      <td>-26.887138</td>\n","      <td>71.311066</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>ببر</td>\n","      <td>11.049977</td>\n","      <td>70.907494</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>غزال</td>\n","      <td>16.920069</td>\n","      <td>37.858723</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>وشق</td>\n","      <td>-0.504379</td>\n","      <td>-0.207813</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>گرگ</td>\n","      <td>45.577942</td>\n","      <td>66.243073</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>لک‌لک</td>\n","      <td>-113.216927</td>\n","      <td>59.772980</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>کیوی</td>\n","      <td>91.251060</td>\n","      <td>-44.386681</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>جگوار</td>\n","      <td>35.659851</td>\n","      <td>-157.787201</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>گاو</td>\n","      <td>33.832932</td>\n","      <td>-104.449661</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>دودو</td>\n","      <td>-36.835255</td>\n","      <td>-12.611772</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>کبک</td>\n","      <td>37.916004</td>\n","      <td>-38.112583</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>شنقار</td>\n","      <td>-26.810232</td>\n","      <td>-32.726875</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>درازدامن</td>\n","      <td>-83.259674</td>\n","      <td>-89.679665</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>دلفین</td>\n","      <td>59.095646</td>\n","      <td>20.444759</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>خرس</td>\n","      <td>-11.816944</td>\n","      <td>106.518707</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>صدف‌خوار</td>\n","      <td>-117.176651</td>\n","      <td>25.100397</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>دجاج</td>\n","      <td>-57.268658</td>\n","      <td>-59.702755</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>بوقلمون</td>\n","      <td>68.381424</td>\n","      <td>-90.813103</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>لانه‌کن</td>\n","      <td>-136.196701</td>\n","      <td>75.190651</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>سمور</td>\n","      <td>-59.971680</td>\n","      <td>87.686676</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>کرکس</td>\n","      <td>-19.198805</td>\n","      <td>13.033937</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>توکان</td>\n","      <td>-61.622128</td>\n","      <td>4.537415</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>تاکین</td>\n","      <td>138.531723</td>\n","      <td>14.713703</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>تاپیر بیرد</td>\n","      <td>-68.881699</td>\n","      <td>-74.525467</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>ویسنت</td>\n","      <td>138.284622</td>\n","      <td>34.565331</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>ماهی</td>\n","      <td>75.712807</td>\n","      <td>-73.990974</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>شترمرغ</td>\n","      <td>-10.092168</td>\n","      <td>-161.997208</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>طاووس</td>\n","      <td>-37.293495</td>\n","      <td>-99.642143</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Word  Component 1  Component 2\n","0      ایگوانا   -91.826584    45.098480\n","1          اسب    26.676863  -132.220734\n","2           خر    -0.695346   -73.723679\n","3         الاغ    -5.944066  -119.264320\n","4        تمساح   -19.752426    46.634068\n","5          شتر    16.000875  -110.453720\n","6         گربه   -76.900780    48.546539\n","7         پلنگ    20.580557   114.023643\n","8        روباه   -44.781902    41.786091\n","9      موزخوار    83.924042   -29.970369\n","10        گوزن    -1.669342    51.262852\n","11        مرغ     73.797386  -118.267479\n","12     لاک‌پشت   -73.737640   107.210884\n","13         شیر   104.045219   -75.137169\n","14       زرافه   -27.329851   133.570709\n","15        عقاب   -95.978836   -18.559546\n","16      بوفالو   -26.887138    71.311066\n","17         ببر    11.049977    70.907494\n","18        غزال    16.920069    37.858723\n","19         وشق    -0.504379    -0.207813\n","20         گرگ    45.577942    66.243073\n","21       لک‌لک  -113.216927    59.772980\n","22        کیوی    91.251060   -44.386681\n","23       جگوار    35.659851  -157.787201\n","24         گاو    33.832932  -104.449661\n","25        دودو   -36.835255   -12.611772\n","26         کبک    37.916004   -38.112583\n","27       شنقار   -26.810232   -32.726875\n","28    درازدامن   -83.259674   -89.679665\n","29       دلفین    59.095646    20.444759\n","30         خرس   -11.816944   106.518707\n","31    صدف‌خوار  -117.176651    25.100397\n","32        دجاج   -57.268658   -59.702755\n","33     بوقلمون    68.381424   -90.813103\n","34     لانه‌کن  -136.196701    75.190651\n","35        سمور   -59.971680    87.686676\n","36        کرکس   -19.198805    13.033937\n","37       توکان   -61.622128     4.537415\n","38       تاکین   138.531723    14.713703\n","39  تاپیر بیرد   -68.881699   -74.525467\n","40       ویسنت   138.284622    34.565331\n","41        ماهی    75.712807   -73.990974\n","42      شترمرغ   -10.092168  -161.997208\n","43       طاووس   -37.293495   -99.642143"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"htDmE3h-zzb-","colab_type":"code","outputId":"0858d38a-b833-4ba7-922a-50323b06aeea","executionInfo":{"status":"ok","timestamp":1588760464606,"user_tz":-270,"elapsed":53834,"user":{"displayName":"Mohammad Reza Taesiri","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiS26mAvXW5ZkJN5GdyvyHDngyAuuXsMuFrSurKv6Y=s64","userId":"13091492792809000649"}},"colab":{"base_uri":"https://localhost:8080/","height":529}},"source":["plot_from_df(tsneDf, plot_title=\"t-SNE\", label_distance=75)"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x504 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}