week-three.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# __Week 3__: Attention & Transformers\n",
    "*** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### Preliminaries:\n",
    "##### Sequence-to-Sequence Models\n",
    "* Takes a sequence of items (such as strings, images, etc.) and outputs another sequence of items\n",
    "    * For example, a __translation__ task: \"I am sad.\" --> \"Yo estoy triste.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sequence-to-sequence models consist of an __encoder__ and a __decoder__ (both of which are Neural Networks).\n",
    "    * The encoder processes each item in the input (turning it into a __context__ vector), then sends to to the decoder.\n",
    "        * The encoder is a recurrent neural network (RNN), which takes at each step an input and a __hidden state__. \n",
    "        * The last hidden state of the encoder is passed to the decoder as a context. \n",
    "    * The decoder then begins processing the output item by item.\n",
    "        * The decoder also maintains hidden states processed at each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from googletrans import Translator, constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "translator = Translator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spanish, German, Tagolog, Farsi, Urdu, Japanese\n",
    "langs = ['es', 'de', 'tl', 'fa', 'ur', 'ja']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "string = \"I am very excited to learn about mathematics and natural language.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estoy muy emocionado de aprender sobre matemáticas y lenguaje natural.\n",
      "\n",
      "Ich freue mich sehr, etwas über Mathematik und natürliche Sprache zu lernen.\n",
      "\n",
      "Labis akong nasasabik na malaman ang tungkol sa matematika at likas na wika.\n",
      "\n",
      "من بسیار مشتاق هستم که ریاضیات و زبان طبیعی را بیاموزم.\n",
      "\n",
      "میں ریاضی اور قدرتی زبان کے بارے میں جان کر بہت پرجوش ہوں۔\n",
      "\n",
      "私は数学と自然言語について学ぶことにとても興奮しています。\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for lang in langs:\n",
    "    translation = translator.translate(string, dest=lang)\n",
    "    print(translation.text)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'translation': [['私は数学と自然言語について学ぶことにとても興奮しています。',\n",
       "   'I am very excited to learn about mathematics and natural language.',\n",
       "   None,\n",
       "   None,\n",
       "   3,\n",
       "   None,\n",
       "   None,\n",
       "   [[]],\n",
       "   [[['9588ca5d94759e1e85ee26c1b641b1e3', 'kgmt_en_ja_2020q3.md']]]],\n",
       "  [None,\n",
       "   None,\n",
       "   'Watashi wa sūgaku to shizen gengo ni tsuite manabu koto ni totemo kōfun shite imasu.']],\n",
       " 'all-translations': None,\n",
       " 'original-language': 'en',\n",
       " 'possible-translations': [['I am very excited to learn about mathematics and natural language.',\n",
       "   None,\n",
       "   [['私は数学と自然言語について学ぶことにとても興奮しています。', 0, True, False, [3]],\n",
       "    ['私は非常に数学と自然言語について学ぶために興奮しています。', 0, True, False, [0]]],\n",
       "   [[0, 66]],\n",
       "   'I am very excited to learn about mathematics and natural language.',\n",
       "   0,\n",
       "   0]],\n",
       " 'confidence': 1,\n",
       " 'possible-mistakes': None,\n",
       " 'language': [['en'], None, [1], ['en']],\n",
       " 'synonyms': None,\n",
       " 'definitions': None,\n",
       " 'examples': None,\n",
       " 'see-also': None}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translation.extra_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attention\n",
    "* Sequence-to-sequence models tend to struggle with longer sentences.\n",
    "* Attention work with RNN's to allow for the model to *give more attention* to relevant parts of the sequence as needed.\n",
    "    * Attention models pass all hidden states to the decoder, as opposed to only the last hidden state like in sequence-to-sequence models. \n",
    "    * Each hidden state is then associated with a specific item within an input and scored to amplify hidden states with high scores and drown out those with low scores.\n",
    "    * Can be thought of as mapping a query and a set of key-value pairs to an output\n",
    "    \n",
    "<br>\n",
    "\n",
    "* __Multi-head Attention__ - allows the model to jointly attend to information from different representation subspaces at different positions\n",
    "\n",
    "* __Self-attention__ - used in place of RNN's to perform encodings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"figures/hydra.jpg\" alt=\"multihead\" title=\"multihead\" width=\"600\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModel\n",
    "from bertviz import model_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_transform.weight']\n",
      "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
    "model = AutoModel.from_pretrained(\"distilbert-base-uncased\", output_attentions=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = tokenizer.encode(\"I am very excited to learn about mathematics and natural language.\", return_tensors='pt')\n",
    "outputs = model(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "attention = outputs[-1]  # Output includes attention weights when output_attentions=True\n",
    "tokens = tokenizer.convert_ids_to_tokens(inputs[0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "/**\n",
       " * @fileoverview Transformer Visualization D3 javascript code.\n",
       " *\n",
       " * Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n",
       " *\n",
       " * Change log:\n",
       " *\n",
       " * 02/01/19  Jesse Vig   Initial implementation\n",
       " * 12/31/20  Jesse Vig   Support multiple visualizations in single notebook.\n",
       " * 01/19/21  Jesse Vig   Support light/dark modes\n",
       " * 02/06/21  Jesse Vig   Move require config from separate jupyter notebook step\n",
       " * 05/03/21  Jesse Vig   Adjust visualization height dynamically\n",
       " **/\n",
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0.0033293033484369516, 0.005700793117284775, 0.6523052453994751, 0.2792971730232239]], [[0.039137713611125946, 0.05704456567764282, 0.06291250139474869, 0.019670048728585243, 0.018208421766757965, 0.029313242062926292, 0.029721995815634727, 0.025675322860479355, 0.032286785542964935, 0.02721952274441719, 0.013780350796878338, 0.027072321623563766, 0.3548184633255005, 0.2631387710571289], [0.02994796447455883, 0.04246825352311134, 0.029399340972304344, 0.010352772660553455, 0.004625868983566761, 0.008162971585988998, 0.01133748795837164, 0.0134115070104599, 0.022926896810531616, 0.010223346762359142, 0.013037607073783875, 0.019495435059070587, 0.48911598324775696, 0.2954946756362915], [0.020710695534944534, 0.05831677094101906, 0.035023026168346405, 0.014171664603054523, 0.010749061591923237, 0.024744264781475067, 0.02312457002699375, 0.01431108545511961, 0.01555041503161192, 0.011972373351454735, 0.003909710329025984, 0.009469052776694298, 0.5022571682929993, 0.25569018721580505], 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0.006320470944046974, 0.011570492759346962, 0.021938998252153397, 0.7043273448944092, 0.20253169536590576], [0.00848453026264906, 0.009445834904909134, 0.00782429613173008, 0.0022352002561092377, 0.0019850253593176603, 0.0020164272282272577, 0.003353494219481945, 0.005190784577280283, 0.019746068865060806, 0.006074483972042799, 0.011903395876288414, 0.019422002136707306, 0.6736338138580322, 0.2286846786737442]]]], \"left_text\": [\"[CLS]\", \"i\", \"am\", \"very\", \"excited\", \"to\", \"learn\", \"about\", \"mathematics\", \"and\", \"natural\", \"language\", \".\", \"[SEP]\"], \"right_text\": [\"[CLS]\", \"i\", \"am\", \"very\", \"excited\", \"to\", \"learn\", \"about\", \"mathematics\", \"and\", \"natural\", \"language\", \".\", \"[SEP]\"]}], \"default_filter\": \"0\", \"display_mode\": \"dark\", \"root_div_id\": \"bertviz-2e4dfb9ab9f54b44baf94ff5be9188a4\", \"include_layers\": [0, 1, 2, 3, 4, 5], \"include_heads\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"total_heads\": 12} is a template marker that is replaced by actual params.\n",
       "        const config = {};\n",
       "\n",
       "        const MIN_X = 0;\n",
       "        const MIN_Y = 0;\n",
       "        const DIV_WIDTH = 970;\n",
       "        const THUMBNAIL_PADDING = 5;\n",
       "        const DETAIL_WIDTH = 300;\n",
       "        const DETAIL_ATTENTION_WIDTH = 140;\n",
       "        const DETAIL_BOX_WIDTH = 80;\n",
       "        const DETAIL_BOX_HEIGHT = 18;\n",
       "        const DETAIL_PADDING = 28;\n",
       "        const ATTN_PADDING = 0;\n",
       "        const DETAIL_HEADING_HEIGHT = 25;\n",
       "        const DETAIL_HEADING_TEXT_SIZE = 15;\n",
       "        const TEXT_SIZE = 13;\n",
       "        const LAYER_COLORS = d3.schemeCategory10;\n",
       "        const PALETTE = {\n",
       "            'light': {\n",
       "                'text': 'black',\n",
       "                'background': 'white',\n",
       "                'highlight': '#F5F5F5'\n",
       "            },\n",
       "            'dark': {\n",
       "                'text': '#bbb',\n",
       "                'background': 'black',\n",
       "                'highlight': '#222'\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function render() {\n",
       "\n",
       "            // Set global state variables\n",
       "\n",
       "            var attData = config.attention[config.filter];\n",
       "            config.leftText = attData.left_text;\n",
       "            config.rightText = attData.right_text;\n",
       "            config.attn = attData.attn;\n",
       "            config.numLayers = config.attn.length;\n",
       "            config.numHeads = config.attn[0].length;\n",
       "            config.thumbnailBoxHeight = 7 * (12 / config.totalHeads);\n",
       "            config.thumbnailHeight = Math.max(config.leftText.length, config.rightText.length) * config.thumbnailBoxHeight + 2 * THUMBNAIL_PADDING;\n",
       "            config.thumbnailWidth = DIV_WIDTH / config.totalHeads;\n",
       "            config.detailHeight = Math.max(config.leftText.length, config.rightText.length) * DETAIL_BOX_HEIGHT + 2 * DETAIL_PADDING + DETAIL_HEADING_HEIGHT;\n",
       "            config.divHeight = Math.max(config.numLayers * config.thumbnailHeight, config.detailHeight);\n",
       "\n",
       "            const vis = $(`#${config.rootDivId} #vis`)\n",
       "            vis.empty();\n",
       "            vis.attr(\"height\", config.divHeight);\n",
       "            config.svg = d3.select(`#${config.rootDivId} #vis`)\n",
       "                .append('svg')\n",
       "                .attr(\"width\", DIV_WIDTH)\n",
       "                .attr(\"height\", config.divHeight)\n",
       "              .attr(\"fill\", getBackgroundColor());\n",
       "\n",
       "            var i;\n",
       "            var j;\n",
       "            for (i = 0; i < config.numLayers; i++) {\n",
       "                for (j = 0; j < config.numHeads; j++) {\n",
       "                    renderThumbnail(i, j);\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function renderThumbnail(layerIndex, headIndex) {\n",
       "            var x = headIndex * config.thumbnailWidth;\n",
       "            var y = layerIndex * config.thumbnailHeight;\n",
       "            renderThumbnailAttn(x, y, config.attn[layerIndex][headIndex], layerIndex, headIndex);\n",
       "        }\n",
       "\n",
       "        function renderDetail(att, layerIndex, headIndex) {\n",
       "            var xOffset = .8 * config.thumbnailWidth;\n",
       "            var maxX = DIV_WIDTH;\n",
       "            var maxY = config.divHeight;\n",
       "            var leftPos = (headIndex / config.totalHeads) * DIV_WIDTH\n",
       "            var x = leftPos + THUMBNAIL_PADDING + xOffset;\n",
       "            if (x < MIN_X) {\n",
       "                x = MIN_X;\n",
       "            } else if (x + DETAIL_WIDTH > maxX) {\n",
       "                x = leftPos + THUMBNAIL_PADDING - DETAIL_WIDTH + 8;\n",
       "            }\n",
       "            var posLeftText = x;\n",
       "            var posAttention = posLeftText + DETAIL_BOX_WIDTH;\n",
       "            var posRightText = posAttention + DETAIL_ATTENTION_WIDTH;\n",
       "            var thumbnailHeight = Math.max(config.leftText.length, config.rightText.length) * config.thumbnailBoxHeight + 2 * THUMBNAIL_PADDING;\n",
       "            var yOffset = 20;\n",
       "            var y = layerIndex * thumbnailHeight + THUMBNAIL_PADDING + yOffset;\n",
       "            if (y < MIN_Y) {\n",
       "                y = MIN_Y;\n",
       "            } else if (y + config.detailHeight > maxY) {\n",
       "                y = maxY - config.detailHeight;\n",
       "            }\n",
       "            renderDetailFrame(x, y, layerIndex);\n",
       "            renderDetailHeading(x, y + Math.max(config.leftText.length, config.rightText.length) *\n",
       "                DETAIL_BOX_HEIGHT, layerIndex, headIndex);\n",
       "            renderDetailText(config.leftText, \"leftText\", posLeftText, y + DETAIL_PADDING, layerIndex);\n",
       "            renderDetailAttn(posAttention, y + DETAIL_PADDING, att, layerIndex, headIndex);\n",
       "            renderDetailText(config.rightText, \"rightText\", posRightText, y + DETAIL_PADDING, layerIndex);\n",
       "        }\n",
       "\n",
       "        function renderDetailHeading(x, y, layerIndex, headIndex) {\n",
       "            var fillColor = getTextColor();\n",
       "            config.svg.append(\"text\")\n",
       "                .classed(\"detail\", true)\n",
       "                .text('Layer ' + config.layers[layerIndex] + \", Head \" + config.heads[headIndex])\n",
       "                .attr(\"font-size\", DETAIL_HEADING_TEXT_SIZE + \"px\")\n",
       "                .style(\"cursor\", \"default\")\n",
       "                .style(\"-webkit-user-select\", \"none\")\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .attr(\"x\", x + DETAIL_WIDTH / 2)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", y + 40)\n",
       "                .attr(\"height\", DETAIL_HEADING_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_WIDTH)\n",
       "                .attr(\"dy\", DETAIL_HEADING_TEXT_SIZE);\n",
       "        }\n",
       "\n",
       "        function renderDetailText(text, id, x, y, layerIndex) {\n",
       "            var tokenContainer = config.svg.append(\"svg:g\")\n",
       "                .classed(\"detail\", true)\n",
       "                .selectAll(\"g\")\n",
       "                .data(text)\n",
       "                .enter()\n",
       "                .append(\"g\");\n",
       "\n",
       "            var fillColor = getTextColor();\n",
       "\n",
       "            tokenContainer.append(\"rect\")\n",
       "                .classed(\"highlight\", true)\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .style(\"opacity\", 0.0)\n",
       "                .attr(\"height\", DETAIL_BOX_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_BOX_WIDTH)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", function (d, i) {\n",
       "                    return y + i * DETAIL_BOX_HEIGHT;\n",
       "                });\n",
       "\n",
       "            var textContainer = tokenContainer.append(\"text\")\n",
       "                .classed(\"token\", true)\n",
       "                .text(function (d) {\n",
       "                    return d;\n",
       "                })\n",
       "                .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                .style(\"cursor\", \"default\")\n",
       "                .style(\"-webkit-user-select\", \"none\")\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", function (d, i) {\n",
       "                    return i * DETAIL_BOX_HEIGHT + y;\n",
       "                })\n",
       "                .attr(\"height\", DETAIL_BOX_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_BOX_WIDTH)\n",
       "                .attr(\"dy\", TEXT_SIZE);\n",
       "\n",
       "            if (id == \"leftText\") {\n",
       "                textContainer.style(\"text-anchor\", \"end\")\n",
       "                    .attr(\"dx\", DETAIL_BOX_WIDTH - 2);\n",
       "                tokenContainer.on(\"mouseover\", function (d, index) {\n",
       "                    highlightSelection(index);\n",
       "                });\n",
       "                tokenContainer.on(\"mouseleave\", function () {\n",
       "                    unhighlightSelection();\n",
       "                });\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function highlightSelection(index) {\n",
       "            config.svg.select(\"#leftText\")\n",
       "                .selectAll(\".highlight\")\n",
       "                .style(\"opacity\", function (d, i) {\n",
       "                    return i == index ? 1.0 : 0.0;\n",
       "                });\n",
       "            config.svg.selectAll(\".attn-line-group\")\n",
       "                .style(\"opacity\", function (d, i) {\n",
       "                    return i == index ? 1.0 : 0.0;\n",
       "                });\n",
       "        }\n",
       "\n",
       "        function unhighlightSelection() {\n",
       "            config.svg.select(\"#leftText\")\n",
       "                .selectAll(\".highlight\")\n",
       "                .style(\"opacity\", 0.0);\n",
       "            config.svg.selectAll(\".attn-line-group\")\n",
       "                .style(\"opacity\", 1);\n",
       "        }\n",
       "\n",
       "        function renderThumbnailAttn(x, y, att, layerIndex, headIndex) {\n",
       "\n",
       "            var attnContainer = config.svg.append(\"svg:g\");\n",
       "\n",
       "            var attnBackground = attnContainer.append(\"rect\")\n",
       "                .attr(\"id\", 'attn_background_' + layerIndex + \"_\" + headIndex)\n",
       "                .classed(\"attn_background\", true)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.thumbnailHeight)\n",
       "                .attr(\"width\", config.thumbnailWidth)\n",
       "                .attr(\"stroke-width\", 2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", 0)\n",
       "                .attr(\"fill\", getBackgroundColor());\n",
       "            var x1 = x + THUMBNAIL_PADDING;\n",
       "            var x2 = x1 + config.thumbnailWidth - 14;\n",
       "            var y1 = y + THUMBNAIL_PADDING;\n",
       "\n",
       "            attnContainer.selectAll(\"g\")\n",
       "                .data(att)\n",
       "                .enter()\n",
       "                .append(\"g\") // Add group for each source token\n",
       "                .attr(\"source-index\", function (d, i) { // Save index of source token\n",
       "                    return i;\n",
       "                })\n",
       "                .selectAll(\"line\")\n",
       "                .data(function (d) { // Loop over all target tokens\n",
       "                    return d;\n",
       "                })\n",
       "                .enter() // When entering\n",
       "                .append(\"line\")\n",
       "                .attr(\"x1\", x1)\n",
       "                .attr(\"y1\", function (d) {\n",
       "                    var sourceIndex = +this.parentNode.getAttribute(\"source-index\");\n",
       "                    return y1 + (sourceIndex + .5) * config.thumbnailBoxHeight;\n",
       "                })\n",
       "                .attr(\"x2\", x2)\n",
       "                .attr(\"y2\", function (d, targetIndex) {\n",
       "                    return y1 + (targetIndex + .5) * config.thumbnailBoxHeight;\n",
       "                })\n",
       "                .attr(\"stroke-width\", 2.2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", function (d) {\n",
       "                    return d;\n",
       "                });\n",
       "\n",
       "            var clickRegion = attnContainer.append(\"rect\")\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.thumbnailHeight)\n",
       "                .attr(\"width\", config.thumbnailWidth)\n",
       "                .style(\"opacity\", 0);\n",
       "\n",
       "            clickRegion.on(\"click\", function (d, index) {\n",
       "                var attnBackgroundOther = config.svg.selectAll(\".attn_background\");\n",
       "                attnBackgroundOther.attr(\"fill\", getBackgroundColor());\n",
       "                attnBackgroundOther.attr(\"stroke-opacity\", 0);\n",
       "\n",
       "                config.svg.selectAll(\".detail\").remove();\n",
       "                if (config.detail_layer != layerIndex || config.detail_head != headIndex) {\n",
       "                    renderDetail(att, layerIndex, headIndex);\n",
       "                    config.detail_layer = layerIndex;\n",
       "                    config.detail_head = headIndex;\n",
       "                    attnBackground.attr(\"fill\", getHighlightColor());\n",
       "                    attnBackground.attr(\"stroke-opacity\", .8);\n",
       "                } else {\n",
       "                    config.detail_layer = null;\n",
       "                    config.detail_head = null;\n",
       "                    attnBackground.attr(\"fill\", getBackgroundColor());\n",
       "                    attnBackground.attr(\"stroke-opacity\", 0);\n",
       "                }\n",
       "            });\n",
       "\n",
       "            clickRegion.on(\"mouseover\", function (d) {\n",
       "                d3.select(this).style(\"cursor\", \"pointer\");\n",
       "            });\n",
       "        }\n",
       "\n",
       "        function renderDetailFrame(x, y, layerIndex) {\n",
       "            var detailFrame = config.svg.append(\"rect\")\n",
       "                .classed(\"detail\", true)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.detailHeight)\n",
       "                .attr(\"width\", DETAIL_WIDTH)\n",
       "                .style(\"opacity\", 1)\n",
       "                .attr(\"stroke-width\", 1.5)\n",
       "                .attr(\"stroke-opacity\", 0.7)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex));\n",
       "        }\n",
       "\n",
       "        function renderDetailAttn(x, y, att, layerIndex) {\n",
       "            var attnContainer = config.svg.append(\"svg:g\")\n",
       "                .classed(\"detail\", true)\n",
       "                .attr(\"pointer-events\", \"none\");\n",
       "            attnContainer.selectAll(\"g\")\n",
       "                .data(att)\n",
       "                .enter()\n",
       "                .append(\"g\") // Add group for each source token\n",
       "                .classed('attn-line-group', true)\n",
       "                .attr(\"source-index\", function (d, i) { // Save index of source token\n",
       "                    return i;\n",
       "                })\n",
       "                .selectAll(\"line\")\n",
       "                .data(function (d) { // Loop over all target tokens\n",
       "                    return d;\n",
       "                })\n",
       "                .enter()\n",
       "                .append(\"line\")\n",
       "                .attr(\"x1\", x + ATTN_PADDING)\n",
       "                .attr(\"y1\", function (d) {\n",
       "                    var sourceIndex = +this.parentNode.getAttribute(\"source-index\");\n",
       "                    return y + (sourceIndex + .5) * DETAIL_BOX_HEIGHT;\n",
       "                })\n",
       "                .attr(\"x2\", x + DETAIL_ATTENTION_WIDTH - ATTN_PADDING)\n",
       "                .attr(\"y2\", function (d, targetIndex) {\n",
       "                    return y + (targetIndex + .5) * DETAIL_BOX_HEIGHT;\n",
       "                })\n",
       "                .attr(\"stroke-width\", 2.2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", function (d) {\n",
       "                    return d;\n",
       "                });\n",
       "        }\n",
       "\n",
       "        function getLayerColor(layer) {\n",
       "          return LAYER_COLORS[layer % 10];\n",
       "        }\n",
       "\n",
       "        function getTextColor() {\n",
       "            return PALETTE[config.mode]['text']\n",
       "        }\n",
       "\n",
       "        function getBackgroundColor() {\n",
       "           return PALETTE[config.mode]['background']\n",
       "        }\n",
       "\n",
       "        function getHighlightColor() {\n",
       "           return PALETTE[config.mode]['highlight']\n",
       "        }\n",
       "\n",
       "        function initialize() {\n",
       "            config.attention = params['attention'];\n",
       "            config.filter = params['default_filter'];\n",
       "            config.mode = params['display_mode'];\n",
       "            config.layers = params['include_layers']\n",
       "            config.heads = params['include_heads']\n",
       "            config.totalHeads = params['total_heads']\n",
       "            config.rootDivId = params['root_div_id'];\n",
       "            $(`#${config.rootDivId} #filter`).on('change', function (e) {\n",
       "                config.filter = e.currentTarget.value;\n",
       "                render();\n",
       "            });\n",
       "        }\n",
       "\n",
       "        initialize();\n",
       "        render();\n",
       "\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_view(attention, tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformers\n",
    "* Used to boost the speed in which Attention models are trained and lend themselves well to parallelization\n",
    "* Rely solely on self-attention to compute representations of inputs and outputs\n",
    "<br>\n",
    "* Starts by creating embeddings for each word, then using self-attention aggregates information from all other words, generating a new representation per word informed by the entire context\n",
    "    *  This step is then repeated multiple times in parallel for all words, successively generating new representations\n",
    "    * The decoder then works similarly, but sequentially "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"figures/transformer.png\" alt=\"transformer example\" title=\"weights\" width=\"900\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformer Tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Question Answering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "question_answerer = pipeline('question-answering')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "context = \"Spongebob is Patrick's very best friend. Squidward hates Patrick. \\\n",
    "Spongebob is also Sally's best friend. Sally is not Patrick's best friend. \\\n",
    "Mr. Krabs loves both his girlfriend Ms. Puff and his daughter Pearl.\"\n",
    "\n",
    "questions = [\"Who is Patrick's best friend?\", \"Does anyone hate Patrick?\", \"Who is Mr. Krabs daughter?\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Who is Patrick's best friend?\n",
      "Answer: 'Spongebob', score: 0.889\n",
      "\n",
      "Does anyone hate Patrick?\n",
      "Answer: 'Squidward', score: 0.725\n",
      "\n",
      "Who is Mr. Krabs daughter?\n",
      "Answer: 'Pearl', score: 0.9661\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for q in questions:\n",
    "    result = question_answerer(question=q, context=context)\n",
    "    print(q)\n",
    "    print(f\"Answer: '{result['answer']}', score: {round(result['score'], 4)}\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = pipeline(\"sentiment-analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = classifier(\"I never smile when I'm around you.\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: NEGATIVE, with score: 0.9373\n"
     ]
    }
   ],
   "source": [
    "print(f\"label: {result['label']}, with score: {round(result['score'], 4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = [\"I hate your guts.\", \"I love machine learning.\", \\\n",
    "             \"You are the absolute worst.\", \"The sun is shining and the birds are singing.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I hate your guts.\n",
      "label: NEGATIVE, with score: 0.9993\n",
      "\n",
      "I love machine learning.\n",
      "label: POSITIVE, with score: 0.9998\n",
      "\n",
      "You are the absolute worst.\n",
      "label: NEGATIVE, with score: 0.9998\n",
      "\n",
      "The sun is shining and the birds are singing.\n",
      "label: POSITIVE, with score: 0.9998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for s in sentences:\n",
    "    result = classifier(s)[0]\n",
    "    print(s)\n",
    "    print(f\"label: {result['label']}, with score: {round(result['score'], 4)}\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Text Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_generator = pipeline(\"text-generation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "starts = [\"If you go to New York\", \"I believe the next president will be\", \"Tomorrow brings\", \"My wife is\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If you go to New York City, you'll see that the city is a very different place. It's a very different place. It's a very different place. It\n",
      "\n",
      "\n",
      "I believe the next president will be a man of action, not a man of fear.\"\n",
      "\n",
      "The president's first major speech to Congress was on the economy, which he\n",
      "\n",
      "\n",
      "Tomorrow brings us to the final chapter of the story.\n",
      "\n",
      "The story begins with the arrival of the first of the new heroes, the Dark Lord, who is the son\n",
      "\n",
      "\n",
      "My wife is a very good cook and I love to cook. I have a lot of friends who are very good at cooking and I love to cook. I have a lot\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gen_texts = []\n",
    "for s in starts:\n",
    "    x = text_generator(s, max_length=35, do_sample=False)\n",
    "    gen_texts.append(x[0]['generated_text'])\n",
    "i = -1\n",
    "for gt in gen_texts:\n",
    "    i = i+1\n",
    "    print(str(gt))\n",
    "    print(\"\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sources:\n",
    "[1] https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ <br>\n",
    "[2] https://jalammar.github.io/illustrated-transformer/ <br>\n",
    "[3] https://nlp.seas.harvard.edu/2018/04/03/attention.html <br>\n",
    "[4] https://github.com/terryyin/translate-python <br>\n",
    "[5] https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html <br>\n",
    "[6] https://huggingface.co/transformers/task_summary.html <br>\n",
    "[7] https://www.thepythoncode.com/article/translate-text-in-python"
   ]
  }
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