{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RUymE2l9GZfO"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Hub Authors.\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "code",
    "execution": {
     "iopub.execute_input": "2023-05-23T08:45:40.229896Z",
     "iopub.status.busy": "2023-05-23T08:45:40.229417Z",
     "iopub.status.idle": "2023-05-23T08:45:40.233221Z",
     "shell.execute_reply": "2023-05-23T08:45:40.232681Z"
    },
    "id": "JMyTNwSJGGWg"
   },
   "outputs": [],
   "source": [
    "# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# =============================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "co7MV6sX7Xto"
   },
   "source": [
    "# Universal Sentence Encoder\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MfBg1C5NB3X0"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://tfhub.dev/s?q=google%2Funiversal-sentence-encoder%2F4%20OR%20google%2Funiversal-sentence-encoder-large%2F5\"><img src=\"https://www.tensorflow.org/images/hub_logo_32px.png\" />See TF Hub models</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eAVQGidpL8v5"
   },
   "source": [
    "This notebook illustrates how to access the Universal Sentence Encoder and use it for sentence similarity and sentence classification tasks.\n",
    "\n",
    "The Universal Sentence Encoder makes getting sentence level embeddings as easy as it has historically been to lookup the embeddings for individual words. The sentence embeddings can then be trivially used to compute sentence level meaning similarity as well as to enable better performance on downstream classification tasks using less supervised training data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pOTzp8O36CyQ"
   },
   "source": [
    "## Setup\n",
    "\n",
    "This section sets up the environment for access to the Universal Sentence Encoder on TF Hub and provides examples of applying the encoder to words, sentences, and paragraphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-23T08:45:40.236557Z",
     "iopub.status.busy": "2023-05-23T08:45:40.236310Z",
     "iopub.status.idle": "2023-05-23T08:45:41.895685Z",
     "shell.execute_reply": "2023-05-23T08:45:41.894766Z"
    },
    "id": "lVjNK8shFKOC"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip3 install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "63Pd3nJnTl-i"
   },
   "source": [
    "More detailed information about installing Tensorflow can be found at [https://www.tensorflow.org/install/](https://www.tensorflow.org/install/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2023-05-23T08:45:41.900118Z",
     "iopub.status.busy": "2023-05-23T08:45:41.899501Z",
     "iopub.status.idle": "2023-05-23T08:46:01.889318Z",
     "shell.execute_reply": "2023-05-23T08:46:01.888591Z"
    },
    "id": "zwty8Z6mAkdV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "module https://tfhub.dev/google/universal-sentence-encoder/4 loaded\n"
     ]
    }
   ],
   "source": [
    "#@title Load the Universal Sentence Encoder's TF Hub module\n",
    "from absl import logging\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import tensorflow_hub as hub\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "import seaborn as sns\n",
    "\n",
    "module_url = \"https://tfhub.dev/google/universal-sentence-encoder/4\" #@param [\"https://tfhub.dev/google/universal-sentence-encoder/4\", \"https://tfhub.dev/google/universal-sentence-encoder-large/5\"]\n",
    "model = hub.load(module_url)\n",
    "print (\"module %s loaded\" % module_url)\n",
    "def embed(input):\n",
    "  return model(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-23T08:46:01.927243Z",
     "iopub.status.busy": "2023-05-23T08:46:01.926928Z",
     "iopub.status.idle": "2023-05-23T08:46:03.191676Z",
     "shell.execute_reply": "2023-05-23T08:46:03.190978Z"
    },
    "id": "Q8F4LNGFqOiq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Message: Elephant\n",
      "Embedding size: 512\n",
      "Embedding: [0.008344466798007488, 0.00048083445290103555, 0.06595246493816376, ...]\n",
      "\n",
      "Message: I am a sentence for which I would like to get its embedding.\n",
      "Embedding size: 512\n",
      "Embedding: [0.0508086197078228, -0.01652432046830654, 0.01573782041668892, ...]\n",
      "\n",
      "Message: Universal Sentence Encoder embeddings also support short paragraphs. There is no hard limit on how long the paragraph is. Roughly, the longer the more 'diluted' the embedding will be.\n",
      "Embedding size: 512\n",
      "Embedding: [-0.02833269163966179, -0.05586216598749161, -0.012941463850438595, ...]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#@title Compute a representation for each message, showing various lengths supported.\n",
    "word = \"Elephant\"\n",
    "sentence = \"I am a sentence for which I would like to get its embedding.\"\n",
    "paragraph = (\n",
    "    \"Universal Sentence Encoder embeddings also support short paragraphs. \"\n",
    "    \"There is no hard limit on how long the paragraph is. Roughly, the longer \"\n",
    "    \"the more 'diluted' the embedding will be.\")\n",
    "messages = [word, sentence, paragraph]\n",
    "\n",
    "# Reduce logging output.\n",
    "logging.set_verbosity(logging.ERROR)\n",
    "\n",
    "message_embeddings = embed(messages)\n",
    "\n",
    "for i, message_embedding in enumerate(np.array(message_embeddings).tolist()):\n",
    "  print(\"Message: {}\".format(messages[i]))\n",
    "  print(\"Embedding size: {}\".format(len(message_embedding)))\n",
    "  message_embedding_snippet = \", \".join(\n",
    "      (str(x) for x in message_embedding[:3]))\n",
    "  print(\"Embedding: [{}, ...]\\n\".format(message_embedding_snippet))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BnvjATdy64eR"
   },
   "source": [
    "# Semantic Textual Similarity Task Example\n",
    "\n",
    "The embeddings produced by the Universal Sentence Encoder are approximately normalized. The semantic similarity of two sentences can be trivially computed as the inner product of the encodings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-23T08:46:03.195805Z",
     "iopub.status.busy": "2023-05-23T08:46:03.195164Z",
     "iopub.status.idle": "2023-05-23T08:46:03.199886Z",
     "shell.execute_reply": "2023-05-23T08:46:03.199327Z"
    },
    "id": "h1FFCTKm7ba4"
   },
   "outputs": [],
   "source": [
    "def plot_similarity(labels, features, rotation):\n",
    "  corr = np.inner(features, features)\n",
    "  sns.set(font_scale=1.2)\n",
    "  g = sns.heatmap(\n",
    "      corr,\n",
    "      xticklabels=labels,\n",
    "      yticklabels=labels,\n",
    "      vmin=0,\n",
    "      vmax=1,\n",
    "      cmap=\"YlOrRd\")\n",
    "  g.set_xticklabels(labels, rotation=rotation)\n",
    "  g.set_title(\"Semantic Textual Similarity\")\n",
    "\n",
    "def run_and_plot(messages_):\n",
    "  message_embeddings_ = embed(messages_)\n",
    "  plot_similarity(messages_, message_embeddings_, 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "339tuJ5Pwqqv"
   },
   "source": [
    "## Similarity Visualized\n",
    "Here we show the similarity in a heat map. The final graph is a 9x9 matrix where each entry `[i, j]` is colored based on the inner product of the encodings for sentence `i` and `j`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-23T08:46:03.203168Z",
     "iopub.status.busy": "2023-05-23T08:46:03.202608Z",
     "iopub.status.idle": "2023-05-23T08:46:03.671558Z",
     "shell.execute_reply": "2023-05-23T08:46:03.670922Z"
    },
    "id": "cPMCaxrZwp7t"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "messages = [\n",
    "    # Smartphones\n",
    "    \"I like my phone\",\n",
    "    \"My phone is not good.\",\n",
    "    \"Your cellphone looks great.\",\n",
    "\n",
    "    # Weather\n",
    "    \"Will it snow tomorrow?\",\n",
    "    \"Recently a lot of hurricanes have hit the US\",\n",
    "    \"Global warming is real\",\n",
    "\n",
    "    # Food and health\n",
    "    \"An apple a day, keeps the doctors away\",\n",
    "    \"Eating strawberries is healthy\",\n",
    "    \"Is paleo better than keto?\",\n",
    "\n",
    "    # Asking about age\n",
    "    \"How old are you?\",\n",
    "    \"what is your age?\",\n",
    "]\n",
    "\n",
    "run_and_plot(messages)\n",
    "               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6FjdeCqPJeg-"
   },
   "source": [
    "## Evaluation: STS (Semantic Textual Similarity) Benchmark\n",
    "\n",
    "The [**STS Benchmark**](https://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) provides an intrinsic evaluation of the degree to which similarity scores computed using sentence embeddings align with human judgements. The benchmark requires systems to return similarity scores for a diverse selection of sentence pairs. [Pearson correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) is then used to evaluate the quality of the machine similarity scores against human judgements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q5nuBbI1iFQR"
   },
   "source": [
    "### Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-23T08:46:03.677829Z",
     "iopub.status.busy": "2023-05-23T08:46:03.677315Z",
     "iopub.status.idle": "2023-05-23T08:46:05.179800Z",
     "shell.execute_reply": "2023-05-23T08:46:05.179150Z"
    },
    "id": "VOs8ZfOnJeBF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  8192/409630 [..............................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 16384/409630 [>.............................] - ETA: 2s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32768/409630 [=>............................] - ETA: 2s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 57344/409630 [===>..........................] - ETA: 2s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "106496/409630 [======>.......................] - ETA: 1s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "172032/409630 [===========>..................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245760/409630 [================>.............] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "270336/409630 [==================>...........] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "352256/409630 [========================>.....] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "409630/409630 [==============================] - 1s 2us/step\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "import scipy\n",
    "import math\n",
    "import csv\n",
    "\n",
    "sts_dataset = tf.keras.utils.get_file(\n",
    "    fname=\"Stsbenchmark.tar.gz\",\n",
    "    origin=\"http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz\",\n",
    "    extract=True)\n",
    "sts_dev = pandas.read_table(\n",
    "    os.path.join(os.path.dirname(sts_dataset), \"stsbenchmark\", \"sts-dev.csv\"),\n",
    "    skip_blank_lines=True,\n",
    "    usecols=[4, 5, 6],\n",
    "    names=[\"sim\", \"sent_1\", \"sent_2\"])\n",
    "sts_test = pandas.read_table(\n",
    "    os.path.join(\n",
    "        os.path.dirname(sts_dataset), \"stsbenchmark\", \"sts-test.csv\"),\n",
    "    quoting=csv.QUOTE_NONE,\n",
    "    skip_blank_lines=True,\n",
    "    usecols=[4, 5, 6],\n",
    "    names=[\"sim\", \"sent_1\", \"sent_2\"])\n",
    "# cleanup some NaN values in sts_dev\n",
    "sts_dev = sts_dev[[isinstance(s, str) for s in sts_dev['sent_2']]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8OKy8WhnKRe_"
   },
   "source": [
    "### Evaluate Sentence Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-23T08:46:05.183733Z",
     "iopub.status.busy": "2023-05-23T08:46:05.183118Z",
     "iopub.status.idle": "2023-05-23T08:46:05.893299Z",
     "shell.execute_reply": "2023-05-23T08:46:05.892572Z"
    },
    "id": "W-q2r7jyZGb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation coefficient = 0.8036394568291783\n",
      "p-value = 0.0\n"
     ]
    }
   ],
   "source": [
    "sts_data = sts_dev #@param [\"sts_dev\", \"sts_test\"] {type:\"raw\"}\n",
    "\n",
    "def run_sts_benchmark(batch):\n",
    "  sts_encode1 = tf.nn.l2_normalize(embed(tf.constant(batch['sent_1'].tolist())), axis=1)\n",
    "  sts_encode2 = tf.nn.l2_normalize(embed(tf.constant(batch['sent_2'].tolist())), axis=1)\n",
    "  cosine_similarities = tf.reduce_sum(tf.multiply(sts_encode1, sts_encode2), axis=1)\n",
    "  clip_cosine_similarities = tf.clip_by_value(cosine_similarities, -1.0, 1.0)\n",
    "  scores = 1.0 - tf.acos(clip_cosine_similarities) / math.pi\n",
    "  \"\"\"Returns the similarity scores\"\"\"\n",
    "  return scores\n",
    "\n",
    "dev_scores = sts_data['sim'].tolist()\n",
    "scores = []\n",
    "for batch in np.array_split(sts_data, 10):\n",
    "  scores.extend(run_sts_benchmark(batch))\n",
    "\n",
    "pearson_correlation = scipy.stats.pearsonr(scores, dev_scores)\n",
    "print('Pearson correlation coefficient = {0}\\np-value = {1}'.format(\n",
    "    pearson_correlation[0], pearson_correlation[1]))"
   ]
  }
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