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neoyipeng2018/howdotokenizersviewemojis.ipynb

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"<a href=\"https://colab.research.google.com/gist/neoyipeng2018/cb6b5bc10624ae6fcb9b0e3c76cb01f0/howdotokenizersviewemojis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QRWdD5nusQsk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 627
},
"outputId": "7fca1e1b-959c-43e2-86b4-d4ec3c90465f"
},
"source": [
"!pip install transformers"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting transformers\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ae/05/c8c55b600308dc04e95100dc8ad8a244dd800fe75dfafcf1d6348c6f6209/transformers-3.1.0-py3-none-any.whl (884kB)\n",
"\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 890kB 2.9MB/s \n",
"\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.4)\n",
"Collecting sacremoses\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
"\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 890kB 10.1MB/s \n",
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"Collecting tokenizers==0.8.1.rc2\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/80/83/8b9fccb9e48eeb575ee19179e2bdde0ee9a1904f97de5f02d19016b8804f/tokenizers-0.8.1rc2-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
"\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 3.0MB 17.1MB/s \n",
"\u001b[?25hCollecting sentencepiece!=0.1.92\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
"\u001b[K |β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.1MB 28.8MB/s \n",
"\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (1.15.0)\n",
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
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"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
"Building wheels for collected packages: sacremoses\n",
" Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893257 sha256=fe9c291dd32f80a79b9290a0fe5e070a30d3611ceaaf22116acad845c6aee3ff\n",
" Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
"Successfully built sacremoses\n",
"Installing collected packages: sacremoses, tokenizers, sentencepiece, transformers\n",
"Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.8.1rc2 transformers-3.1.0\n"
],
"name": "stdout"
}
]
},
{
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"height": 682,
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},
"outputId": "ecefc1c8-4a5e-40f1-a70f-fe126a769076"
},
"source": [
"from transformers import AutoTokenizer\n",
"BERTtokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
"ALBERTtokenizer = AutoTokenizer.from_pretrained(\"albert-base-v1\")\n",
"XLNETtokenizer=AutoTokenizer.from_pretrained(\"xlnet-base-cased\")\n",
"ROBERTAtokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")\n",
"GPTtokenizer = AutoTokenizer.from_pretrained(\"openai-gpt\")"
],
"execution_count": 2,
"outputs": [
{
"output_type": "display_data",
"data": {
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"model_id": "38dbfeaa1d7847aa90e8e595f6c296c2",
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"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/transformers/configuration_xlnet.py:211: FutureWarning: This config doesn't use attention memories, a core feature of XLNet. Consider setting `men_len` to a non-zero value, for example `xlnet = XLNetLMHeadModel.from_pretrained('xlnet-base-cased'', mem_len=1024)`, for accurate training performance as well as an order of magnitude faster inference. Starting from version 3.5.0, the default parameter will be 1024, following the implementation in https://arxiv.org/abs/1906.08237\n",
" FutureWarning,\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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},
{
"output_type": "stream",
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"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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"metadata": {
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},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
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"metadata": {
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},
{
"output_type": "stream",
"text": [
"ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4KIDSKW7hamk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104
},
"outputId": "e75f71d0-d922-4059-b658-910c24b6b4d3"
},
"source": [
"print('WordPiece - BERT:',BERTtokenizer.encode('😑'))\n",
"print('SentencePiece - ALBERT:',ALBERTtokenizer.encode('😑'))\n",
"print('SentencePiece - XLNET:',XLNETtokenizer.encode('😑'))\n",
"print('Byte Level Byte Pair Encoding - ROBERTA:',ROBERTAtokenizer.encode('😑'))\n",
"print('Byte Pair Encoding - GPT:',GPTtokenizer.encode('😑'))"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"WordPiece - BERT: [101, 100, 102]\n",
"SentencePiece - ALBERT: [2, 13, 1, 3]\n",
"SentencePiece - XLNET: [17, 0, 4, 3]\n",
"Byte Level Byte Pair Encoding - ROBERTA: [0, 18636, 5543, 2]\n",
"Byte Pair Encoding - GPT: [0]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bM1c0ArLht1E",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104
},
"outputId": "13ec0cad-14d5-49e7-fd41-d295e52322af"
},
"source": [
"print('WordPiece:',BERTtokenizer.tokenize('😑'))\n",
"print('SentencePiece:',ALBERTtokenizer.tokenize('😑'))\n",
"print('XLNET:',XLNETtokenizer.tokenize('😑'))\n",
"print('Byte Level Byte Pair Encoding - ROBERTA:',ROBERTAtokenizer.tokenize('😑'))\n",
"print('Byte Pair Encoding - GPT:',GPTtokenizer.tokenize('😑'))"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"WordPiece: ['[UNK]']\n",
"SentencePiece: ['▁', '😑']\n",
"XLNET: ['▁', '😑']\n",
"Byte Level Byte Pair Encoding - ROBERTA: ['ðŁĺ', '‘']\n",
"Byte Pair Encoding - GPT: ['😑</w>']\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BiXgqgUvi7_2",
"colab_type": "text"
},
"source": [
"So it seems ROBERTA's Byte Level Byte Pair Encoding tokenizer(which is GPT-2's tokenizer) is the only tokenizer that can handle emojis out of the box πŸŽ‰πŸŽ‰πŸŽ‰"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fd78v13_esw9",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104
},
"outputId": "082e8d3e-7e38-4172-991f-bd09c5c235f1"
},
"source": [
"print('WordPiece - BERT:',BERTtokenizer.decode(BERTtokenizer.encode('😑')))\n",
"print('SentencePiece - ALBERT:',ALBERTtokenizer.decode(ALBERTtokenizer.encode('😑')))\n",
"print('SentencePiece - XLNET:',XLNETtokenizer.decode(XLNETtokenizer.encode('😑')))\n",
"print('Byte Level Byte Pair Encoding - ROBERTA:',ROBERTAtokenizer.decode(ROBERTAtokenizer.encode('😑')))\n",
"print('Byte Pair Encoding - GPT:',GPTtokenizer.decode(GPTtokenizer.encode('😑')))"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"WordPiece - BERT: [CLS] [UNK] [SEP]\n",
"SentencePiece - ALBERT: [CLS] <unk>[SEP]\n",
"SentencePiece - XLNET: <unk><sep><cls>\n",
"Byte Level Byte Pair Encoding - ROBERTA: <s>😑</s>\n",
"Byte Pair Encoding - GPT: <unk>\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aScTlpzxXcf7",
"colab_type": "text"
},
"source": [
"To see how this affects downstream tasks, we take a look at some sentences that contain emojis and how other models are affectedb y them."
]
},
{
"cell_type": "code",
"metadata": {
"id": "jgOErx8dZ7aU",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 471
},
"outputId": "7f9934f2-5ae3-400c-efb6-879984ef0d8c"
},
"source": [
"!pip install sentence-transformers"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.6/dist-packages (0.3.5.1)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sentence-transformers) (0.22.2.post1)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from sentence-transformers) (1.4.1)\n",
"Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (from sentence-transformers) (3.2.5)\n",
"Requirement already satisfied: transformers==3.0.2 in /usr/local/lib/python3.6/dist-packages (from sentence-transformers) (3.0.2)\n",
"Requirement already satisfied: torch>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from sentence-transformers) (1.6.0+cu101)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from sentence-transformers) (4.41.1)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from sentence-transformers) (1.18.5)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sentence-transformers) (0.16.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk->sentence-transformers) (1.15.0)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2->sentence-transformers) (3.0.12)\n",
"Requirement already satisfied: tokenizers==0.8.1.rc1 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2->sentence-transformers) (0.8.1rc1)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2->sentence-transformers) (2.23.0)\n",
"Requirement already satisfied: sacremoses in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2->sentence-transformers) (0.0.43)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2->sentence-transformers) (2019.12.20)\n",
"Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2->sentence-transformers) (0.7)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2->sentence-transformers) (20.4)\n",
"Requirement already satisfied: sentencepiece!=0.1.92 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.2->sentence-transformers) (0.1.91)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.2.0->sentence-transformers) (0.16.0)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.2->sentence-transformers) (3.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.2->sentence-transformers) (2020.6.20)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.2->sentence-transformers) (2.10)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.2->sentence-transformers) (1.24.3)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.0.2->sentence-transformers) (7.1.2)\n",
"Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.0.2->sentence-transformers) (2.4.7)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-He5VDIbZ9uC",
"colab_type": "code",
"colab": {}
},
"source": [
"from sentence_transformers import SentenceTransformer, models\n",
"import numpy as np"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Xe_SKGKIZ-Pg",
"colab_type": "code",
"colab": {}
},
"source": [
"#initialise WP and SP model\n",
"word_embedding_model = models.Transformer('bert-base-uncased')\n",
"pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())\n",
"WPmodel = SentenceTransformer(modules=[word_embedding_model, pooling_model])\n",
"\n",
"word_embedding_model = models.Transformer('albert-base-v2')\n",
"pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())\n",
"SPmodel = SentenceTransformer(modules=[word_embedding_model, pooling_model])\n",
"\n",
"word_embedding_model = models.Transformer('roberta-base')\n",
"pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())\n",
"BPEmodel = SentenceTransformer(modules=[word_embedding_model, pooling_model])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "EtA45ldxeSrI",
"colab_type": "code",
"colab": {}
},
"source": [
"def checkSent(sent1,sent2,model):\n",
" \"compare how WP encodes sentences differently from SP\"\n",
" embeddings1 = model.encode(sent1)\n",
" embeddings2 = model.encode(sent2)\n",
"\n",
" combined=np.array([embeddings1,embeddings2])\n",
"\n",
" from sklearn.decomposition import PCA\n",
" pca = PCA(n_components=2)\n",
" pca_result = pca.fit_transform(combined)\n",
"\n",
" print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))\n",
"\n",
" import matplotlib.pyplot as plt\n",
" plt.rcParams[\"figure.figsize\"] = (10,8)\n",
" fig, ax = plt.subplots()\n",
" label=['sent1', 'sent2']\n",
" for i, color in enumerate(['blue','orange']):\n",
" x, y = pca_result[i,0], pca_result[i,1]\n",
" ax.scatter(x, y, c=color, label=label[i],alpha=0.5)\n",
" ax.legend()\n",
" ax.set_title('Comparing\\n'+sent1+' VS \\n'+sent2)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jW2LNkF-eSiX",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 546
},
"outputId": "35a1f387-cff3-4208-ca4c-62be77cb829d"
},
"source": [
"checkSent('This movie was πŸ˜ƒπŸ˜ƒπŸ˜ƒπŸ˜ƒπŸ˜ƒ','This movie was 😠😠😠😠😠',BPEmodel)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Explained variation per principal component: [1.0000000e+00 2.1427512e-13]\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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og+EXUSYChfLY/5Oa5XcAQ5rl04DTOp3rMsoM6Ed187/Fp4Dze+rf2tda/7vMA97eadtJwLUt668Dnqm9hzKY9+SO77VVnGcSJSwMbtZHUOZw273lmBnAQc3yfcD+Lfv2BeY1y9tRpsfYuFm/BDixWR7flDuE8mTpPwMvaynnEOD6Lur4JeCs5r1/AD4DnEoJds8Ao7t435nAN5vlj1Lmh9uxjWt/a8vy0Jblwax8yO67gROa5S9SukYBouP/ceX9d/Tnc/jy5WvNXj3WkpSZNwJ/at0WEa9pWlZmRMRNEfGXa1DedZQfzp23P96UHcDLKD+kVysiXkf5IXRNU86Tmfl05dB/oNxV8mhz3KKOU1N+gG8EDKPMUfVHyg+3AF7e1GlTmhnXM/PqXDmD+q3AuJb6HAT8nhfPCE5EHBoRtzctQv9W665owyGUFgANDH9oWX4aGB71wbeHA9sD9zRdSwdWjumwJFcOqO5oXfljy/5nKH8oAGxFacnp8ECzjcy8lzKP2rsiYmPKL+DapLivpvyfeLhpAX4M+DfgFV3U7wZKmHsTpavoGkoL0h7Avdnc1RURu0fE9U0X31JKa9GYpoyLKDPZX9p0E34tIoZ2cb7W6/meiJgWEbcAX28p75WUCXYBllBa42g+wxkRcWvTst163Yd0sdxfziFpDfT2mKRzgU9l5i7AUcB3uqPQiPgPyi+WvwT+pc23bQ88FhGXN2Mcvt5FANke2D4iftn8wHonQGbeQpnR++Hm9fPMnJuZy4CPU37QL6S0BJxXKfejwM+a+m8CHE1pFWj9XDtQBmO+NTN3psz5tNo7oDqV8WpKM/1/r8n71P9l5u8y8xDKL9TTgMsi4uXdUPRCSsjpsE2zrUNHl9t7gDlNcOrsQUpL0pjM3Kx5bZqZE7o456+AvwDeC9yQmXOa8+7Pyq42KIHsx8DWmTkS+FfKHyVk5rLMPDkzX0dp6T2Q0jVXMyci3hwRx1C6Hf8vsB8wE7g6IsYC76PcNQalm+tvmjFOP6dM2rsvMAX4SEQcFRFv4cV/5PTHc0haA712a2gTBN4C/LAZugOlBYaImEy5k6OzhzJztX3pmfmRJuD8CyVU/EdEfITSZA+li2BaRDwH/D4z30v57G+jjJWYT+m3P4yXBpohlC63SZSWnxsj4g2Uv9J2YGVr0DUR8TZKC9HHm3Lvb+p0LC2zrkfEcZSxGh13M51E6TJ4suXaQOli3AW4o9n+MkpXHhFxIeWv7s6+k5mt4fNg4LIcILdlq30RcSglnC9uGXPWHZOf/gA4PiLuoLSYnghc3LL/UuArwObUW5HIzIcj4mpKa8gJlGf9bAuMy8wbKsc/HREzKM/6OaDZ/CtKS9HhLYeOAP6Umc9GxG7A3wFXA0TEXpQWkzmU2e6X0fX1+Gfge5Su6K/lykljL4yI24DJwMLM/G1Tv/siYiElxP1tZv6uOX5pRLwX2JUyRuyw/nwOSWuoJ/vyKGMU7m6WNwUeXsfyJtFpTFKn/X9V209lTBKlGf+GlvW/p3SrdX7vvwIfaVm/jvLDagot/fyUXyRfaPZd16lO01rWD6OMX9q4ZdtNlLEm84DHKN2Un6T84PvqOl6zXwNv6cl/Z19r/W8zj/qYpItb1sfTjPPp/B5KcFlECSCzacYUVc4zCVjQsj6kKXN8y7abgUOb5eGU8UEdraRnAcM7lXkdJehvsYq6jgTOodyVtbT5Xjx4Fdfjq5Ruv2HN+ieb8l7Zcsz7KN1/TwBXUga1X9zsO4Ryy/tTlK7Es2gZ11M537soY7EOpBnPQ/mj53RKK+/LOh2/cbP9DEpLVse13L8p510D4Ry+fPlq/9UxsK9HNHeLXZmZr2/Wf0VpMflhM15nx8z8zRqUN4kyqPnAZj2A12Tmvc3y1wEy86hO7/sFcFhmzmvZNhi4k/ILZ3HTZTc9M7/d6b3vBA7JzA83d6n9mnLnyNsp45XeSWnuv4oyiHRG89qxKfdLlED0+aasbwB7ZubiLj7jScCTmXl6M27qCkp326IoT9UdkZkP1N5bKesvm3ptmz35Dy0NUM0dfZ+jtHIPpoTOSyhPq35JK1REDKJ0a32QMtZnBaX1+IzMvH+gnENSe3osJEXEDyh/wY6h/FX3T5RxMecAW1IGdV6ambVutlp5N1HGHG1CGeB4OGVw502UVqoAfgN8PJvB3C3v/QWdQlKzfR/KX2xBCTZHZuZzEXEKJTD9uAlfZ1DC0ArgK5l5aROyvkNpKUrgqsz8XFPuxyhdfcsof/UelplLIuJeShdjx9QCt2bmxzrV6SSakNSsf4DSXTeoKe8fM/PWNq/ZSZQWgGPaOV6SJK3Uoy1JkiRJA5VP3JYkSaowJEmSJFX0yCMAxowZk+PHj++JoiVJkrrVjBkzHsnMsZ2390hIGj9+PNOnT++JoiVJkrpVRFTvGre7TZIkqcKQJEmSVGFIkiRJqui1udskSVLfW7ZsGQsWLODZZ5/t66r0uuHDhzNu3DiGDh3a1vGGJEmSNiALFixgxIgRjB8/nk6Tqq/XMpMlS5awYMECtt1227beY3ebJEkbkGeffZbRo0dvUAEJICIYPXr0GrWgGZIkSdrAbGgBqcOafm5DkiRJGlBmzpzJtGnTXli/5557ePOb38ywYcM4/fTTu+08hiRJkjSgdA5Jm2++OWeddRZHHXVUt57HkCRJkro0axacdBJ89KPl66xZ61beU089xQEHHMBOO+3E61//eqZOncqMGTPYc8892WWXXdh33315+OGHAZg0aRJHH300u+22G9tvvz033XQTzz33HCeeeCJTp05l5513ZurUqbziFa9g1113bfuutXZ5d5skSaqaNQtOPx1GjYJx4+DRR8v6UUfBjjuuXZlXXXUVW221FT/96U8BWLp0Kfvttx9XXHEFY8eOZerUqRx33HGcf/75ACxfvpzbb7+dadOmcfLJJ3PttddyyimnMH36dM4+++zu+qhVhiRJklR1+eUlII0aVdY7vl5++dqHpDe84Q18/vOf5+ijj+bAAw9k1KhR3H333eyzzz4ArFixgi233PKF4ydPngzALrvswrx589b2o6wVQ5IkSaqaP7+0ILUaObJsX1vbb789d955J9OmTeP4449n7733ZsKECdxyyy3V44cNGwbA4MGDWb58+dqfeC04JkmSJFVtsw0sXfribUuXlu1ra+HChWy88cYceuihTJkyhdtuu43Fixe/EJKWLVvG7NmzV1nGiBEjeOKJJ9a+Em2yJUmSJFVNnlzGIEFpQVq6tIxLOvzwtS/zrrvuYsqUKQwaNIihQ4dyzjnnMGTIED796U+zdOlSli9fzmc/+1kmTJjQZRl77bUXp556KjvvvDPHHnsse+65JxMnTuTxxx9n0KBBnHnmmcyZM4dNN9107SsKRGauUwE1EydOzOnTp3d7uQA8OgsevByeng8bbwNbT4ZRa9kxKknSBmbu3LnssMMObR8/a1YZgzR/fmlBmjx57ccj9Qe1zx8RMzJzYudjB1ZL0qOzYO7psNEoeNk4eO7Rsr7DUQYlSZJ6wI47DuxQtC4G1pikBy8vAWmjURCDVi4/eHlf10ySJK1nBlZIeno+DB354m1DR5btkiRJ3WhghaSNt4FlnYbZL1tatkuSJHWjgRWStp5cxiE99yjk8yuXt57c1zWTJEnrmYEVkkbtWAZpbzQKnllQvjpoW5Ik9YCBdXcblEBkKJIkaYM1c+ZMFi5cyP777w/AJZdcwmmnnUZmMmLECM455xx22mmndT7PwGpJkiRJG7yZM2cybdq0F9a33XZbbrjhBu666y5OOOEEjjzyyG45T1stSRGxGfBd4PVAAh/NzPokK5Ikaf3RzQ9xfuqpp3j/+9/PggULWLFiBSeccALbbbcdn/vc53jyyScZM2YMF1xwAVtuuSWTJk1i99135/rrr+exxx7jvPPOY/fdd+fEE0/kmWee4eabb+bYY4/lAx/4wAvl77HHHixYsKA7Pnnb3W3fAq7KzPdFxEbAxt1ydkmS1H/1wEOcr7rqKrbaait++tOfArB06VL2228/rrjiCsaOHcvUqVM57rjjOP/88wFYvnw5t99+O9OmTePkk0/m2muv5ZRTTmH69OmcffbZLyn/vPPOY7/99lv7z9xitSEpIkYCfwUcBpCZzwHPdcvZJUlS/9X6EGdY+fXBy9c6JL3hDW/g85//PEcffTQHHnggo0aN4u6772afffYBYMWKFWy55ZYvHD95crmDfZdddmHevHmrLPv666/nvPPO4+abb16runXWTkvStsBi4D8iYidgBvCZzHyqW2ogSZL6p6fnlxakVuv4EOftt9+eO++8k2nTpnH88cez9957M2HCBG65pT6KZ9iwYQAMHjyY5cuXd1nurFmzOOKII/jZz37G6NGj17p+rdoZuD0EeBNwTma+EXgKOKbzQRFxZERMj4jpixcv7pbKSZKkPtQDD3FeuHAhG2+8MYceeihTpkzhtttuY/HixS+EpGXLljF79uxVljFixAieeOKJF9bnz5/P5MmTueiii9h+++3Xum6dtdOStABYkJm3NeuXUQlJmXkucC7AxIkTs9tqKEmS+sbWk8sYJCgtSMuWlnFJrzl8rYu86667mDJlCoMGDWLo0KGcc845DBkyhE9/+tMsXbqU5cuX89nPfpYJEyZ0WcZee+3Fqaeeys4778yxxx7LNddcw5IlS/jEJz4BwJAhQ5g+ffpa17FDZK4+z0TETcARmfk/EXES8PLMnNLV8RMnTszuqJwkSepec+fOZYcddmj/Dd18d1tfq33+iJiRmRM7H9vu3W2fAi5p7my7H/jIOtdSkiT1fxvwQ5zbCkmZORN4ScKSJElaX/nEbUmSpApDkiRJG5h2xiOvj9b0cxuSJEnagAwfPpwlS5ZscEEpM1myZAnDhw9v+z3tDtyWJEnrgXHjxrFgwQI2xGcaDh8+nHHjxq3+wIYhSZKkDcjQoUPZdttt+7oaA4LdbZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRVD2jkoIuYBTwArgOWZObEnKyVJktTX2gpJjb0y85Eeq4kkSVI/YnebJElSRbshKYGrI2JGRBzZkxWSJEnqD9rtbvvfmflQRLwCuCYi7snMG1sPaMLTkQDbbLNNN1dTkiSpd7XVkpSZDzVfFwE/AnarHHNuZk7MzIljx47t3lpKkiT1stWGpIh4eUSM6FgG3gHc3dMVkyRJ6kvtdLe9EvhRRHQc//3MvKpHayVJktTHVhuSMvN+YKdeqIskSVK/4SMAJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSLv9oP4AAA1WSURBVJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJU0XZIiojBEfHriLiyJyskSZLUH6xJS9JngLk9VRFJkqT+pK2QFBHjgAOA7/ZsdSRJkvqHdluSzgS+ADzfg3WRJEnqN1YbkiLiQGBRZs5YzXFHRsT0iJi+ePHibqugJElSX2inJemtwLsjYh5wKbB3RFzc+aDMPDczJ2bmxLFjx3ZzNSVJknrXakNSZh6bmeMyczxwMPDfmXloj9dMkiSpD/mcJEmSpIoha3JwZv4C+EWP1ESSJKkfsSVJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVqw1JETE8Im6PiN9ExOyIOLk3KiZJktSXhrRxzJ+BvTPzyYgYCtwcET/LzFt7uG6SJEl9ZrUhKTMTeLJZHdq8sicrJUmS1NfaGpMUEYMjYiawCLgmM2/r2WpJkiT1rbZCUmauyMydgXHAbhHx+s7HRMSRETE9IqYvXry4u+spSZLUq9bo7rbMfAy4HnhnZd+5mTkxMyeOHTu2u+onSZLUJ9q5u21sRGzWLL8M2Ae4p6crJkmS1JfaubttS+B7ETGYEqr+MzOv7NlqSZIk9a127m6bBbyxF+oiSZLUb/jEbUmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVaw2JEXE1hFxfUTMiYjZEfGZ3qiYJElSXxrSxjHLgc9n5p0RMQKYERHXZOacHq6bJElSn1ltS1JmPpyZdzbLTwBzgVf1dMUkSZL60hqNSYqI8cAbgdt6ojKSJEn9RdshKSI2Af4f8NnMfLyy/8iImB4R0xcvXtyddZQkSep1bYWkiBhKCUiXZObltWMy89zMnJiZE8eOHduddZQkSep17dzdFsB5wNzM/EbPV0mSJKnvtdOS9Fbg74G9I2Jm89q/h+slSZLUp1b7CIDMvBmIXqiLJElSv+ETtyVJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUYUiSJEmqMCRJkiRVGJIkSZIqDEmSJEkVhiRJkqQKQ5IkSVKFIUmSJKnCkCRJklRhSJIkSaowJEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqWJIX1dAkiSp1axZcPnlMH8+bLMNTJ4MO+7Y+/WwJUmSJPUbs2bB6afDo4/CuHHl6+mnl+29zZAkSZL6jcsvh1GjymvQoJXLl1/e+3UxJEmSpH5j/nwYOfLF20aOLNt7myFJkiT1G9tsA0uXvnjb0qVle28zJEmSpH5j8uQyDunRR+H551cuT57c+3UxJEmSpH5jxx3hqKPKOKQFC8rXo47qm7vbfASAJEnqV3bcsW9CUWe2JEmSJFUYkiRJkioMSZIkSRWGJEmSpApDkiRJUoUhSZIkqcKQJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElShSFJkiSpwpAkSZJUEZnZ/YVGLAYeWMu3jwEe6cbq6MW8vj3L69uzvL49x2vbs7y+PWtdr++rM3Ns5409EpLWRURMz8yJfV2P9ZXXt2d5fXuW17fneG17lte3Z/XU9bW7TZIkqcKQJEmSVNEfQ9K5fV2B9ZzXt2d5fXuW17fneG17lte3Z/XI9e13Y5IkSZL6g/7YkiRJktTn+jwkRcTfRsTsiHg+IrocmR4R8yLiroiYGRHTe7OOA9kaXN93RsT/RMS9EXFMb9ZxIIuIzSPimoj4XfN1VBfHrWi+d2dGxI97u54Dyeq+FyNiWERMbfbfFhHje7+WA1cb1/ewiFjc8v16RF/UcyCKiPMjYlFE3N3F/oiIs5prPysi3tTbdRzI2ri+kyJiacv37onres4+D0nA3cBk4MY2jt0rM3f2Nso1strrGxGDgW8D+wGvAw6JiNf1TvUGvGOA6zLztcB1zXrNM8337s6Z+e7eq97A0ub34uHAo5m5HfBN4LTereXAtQb/16e2fL9+t1crObBdALxzFfv3A17bvI4EzumFOq1PLmDV1xfgppbv3VPW9YR9HpIyc25m/k9f12N91eb13Q24NzPvz8zngEuB9/R87dYL7wG+1yx/DzioD+uyPmjne7H1ml8G/HVERC/WcSDz/3oPyswbgT+t4pD3ABdmcSuwWURs2Tu1G/jauL7drs9D0hpI4OqImBERR/Z1ZdYzrwIebFlf0GzT6r0yMx9ulv8AvLKL44ZHxPSIuDUiDFJda+d78YVjMnM5sBQY3Su1G/ja/b/+N0130GURsXXvVG2D4M/anvfmiPhNRPwsIiasa2FDuqNGqxMR1wJbVHYdl5lXtFnM/87MhyLiFcA1EXFPkyo3eN10fdWFVV3f1pXMzIjo6nbRVzffv/8L+O+IuCsz7+vuukrd4CfADzLzzxHxfyitdnv3cZ2kdtxJ+Vn7ZETsD/wXpWtzrfVKSMrMt3dDGQ81XxdFxI8ozcaGJLrl+j4EtP61OK7ZJlZ9fSPijxGxZWY+3DSbL+qijI7v3/sj4hfAGwFD0ku1873YccyCiBgCjASW9E71BrzVXt/MbL2W3wW+1gv12lD4s7YHZebjLcvTIuI7ETEmM9d6TrcB0d0WES+PiBEdy8A7KAOS1T3uAF4bEdtGxEbAwYB3YLXnx8CHm+UPAy9puYuIURExrFkeA7wVmNNrNRxY2vlebL3m7wP+O33gW7tWe307jZF5NzC3F+u3vvsx8KHmLrc9gKUt3fVaRxGxRcf4xIjYjZJx1ukPqF5pSVqViHgv8C/AWOCnETEzM/eNiK2A72bm/pRxHj9qPvsQ4PuZeVWfVXoAaef6ZubyiPgk8HNgMHB+Zs7uw2oPJKcC/xkRhwMPAO8HaB638LHMPALYAfi3iHie8p/21Mw0JFV09b0YEacA0zPzx8B5wEURcS9lEOfBfVfjgaXN6/vpiHg3sJxyfQ/rswoPMBHxA2ASMCYiFgD/BAwFyMx/BaYB+wP3Ak8DH+mbmg5MbVzf9wEfj4jlwDPAwev6B5RP3JYkSaoYEN1tkiRJvc2QJEmSVGFIkiRJqjAkSZIkVRiSJEmSKgxJkiRJFYYkSZKkCkOSJElSxf8HKN55DMhRwHkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Plc0TLIVfAcy",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 581
},
"outputId": "a5dcf677-30b5-43f7-a937-27c847e99d25"
},
"source": [
"checkSent('This movie was πŸ˜ƒπŸ˜ƒπŸ˜ƒπŸ˜ƒπŸ˜ƒ','This movie was 😠😠😠😠😠',WPmodel)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Explained variation per principal component: [nan nan]\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/decomposition/_pca.py:456: RuntimeWarning: invalid value encountered in true_divide\n",
" explained_variance_ratio_ = explained_variance_ / total_var\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_HKFcdEnfAYa",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 581
},
"outputId": "9a0b26c1-ffa8-49d5-820c-4cc7dd749aee"
},
"source": [
"checkSent('This movie was πŸ˜ƒπŸ˜ƒπŸ˜ƒπŸ˜ƒπŸ˜ƒ','This movie was 😠😠😠😠😠',SPmodel)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Explained variation per principal component: [nan nan]\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/decomposition/_pca.py:456: RuntimeWarning: invalid value encountered in true_divide\n",
" explained_variance_ratio_ = explained_variance_ / total_var\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tH97jCYtfIdW",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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