8_universal-sentence-encoder-multilingual-3.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip3 install tensorflow_text>=2.0.0rc0"
      ],
      "metadata": {
        "id": "uGrvtpPK6Bu7"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfXwGmyT4ZPq"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "_4kodLh-4ZPr"
      },
      "outputs": [],
      "source": [
        "import tensorflow_hub as hub\n",
        "import tensorflow_text\n",
        "from tqdm.notebook import tqdm\n",
        "embed = hub.load(\"https://tfhub.dev/google/universal-sentence-encoder-multilingual/3\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDusb3U54ZPs"
      },
      "source": [
        "# JSTS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "oqYvvnud4ZPs",
        "outputId": "6274de18-0bd4-4df5-bcfb-3df3e22ba702"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sentence_pair_id               yjcaptions_id                    sentence1  \\\n",
              "0                0  100312_421853-104611-31624  レンガの建物の前を、乳母車を押した女性が歩いています。   \n",
              "\n",
              "            sentence2  label  \n",
              "0  厩舎で馬と女性とが寄り添っています。    0.0  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentence_pair_id</th>\n",
              "      <th>yjcaptions_id</th>\n",
              "      <th>sentence1</th>\n",
              "      <th>sentence2</th>\n",
              "      <th>label</th>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>100312_421853-104611-31624</td>\n",
              "      <td>レンガの建物の前を、乳母車を押した女性が歩いています。</td>\n",
              "      <td>厩舎で馬と女性とが寄り添っています。</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba5a56e7-bc71-4d90-949d-36609cecf779')\"\n",
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              "      \n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ba5a56e7-bc71-4d90-949d-36609cecf779');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "from urllib.request import urlopen\n",
        "jsts_url = \"https://raw.githubusercontent.com/yahoojapan/JGLUE/main/datasets/jsts-v1.1/valid-v1.1.json\"\n",
        "df = pd.DataFrame([json.loads(line) for line in urlopen(jsts_url).readlines()])\n",
        "df.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayZB6yJB4ZPs",
        "outputId": "10bd5d2f-ff1f-4efc-8bf7-fe8be195b66c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1457, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QNow90Ox4ZPt"
      },
      "source": [
        "## Encode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8E8331kL4ZPt",
        "outputId": "566a6578-6a17-4fe2-a27e-80ebedf071f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([1457, 512]), TensorShape([1457, 512]))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "sentence1_embs = embed(df[\"sentence1\"])\n",
        "sentence2_embs = embed(df[\"sentence2\"])\n",
        "sentence1_embs.shape, sentence2_embs.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QcAwx9SP4ZPu"
      },
      "source": [
        "## Correlation Score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJbbUayK4ZPu",
        "outputId": "d8010195-2db7-43fe-8fa3-4f6b89058994"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7899457524478034"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from scipy.spatial.distance import cosine\n",
        "from scipy.stats import spearmanr\n",
        "df[\"similarity\"] = [1 - cosine(s1, s2) for s1, s2 in zip(sentence1_embs, sentence2_embs)]\n",
        "spearmanr(df[\"similarity\"], df[\"label\"])[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HEnmed0Q4ZPu"
      },
      "source": [
        "# JSICK"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "3o0cIxL-4ZPv",
        "outputId": "42059a71-f32d-4dcb-c983-ae1ca05e8de7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pair_ID  data                                      sentence_A_En  \\\n",
              "0        6  test  There is no boy playing outdoors and there is ...   \n",
              "\n",
              "                                       sentence_B_En entailment_label_En  \\\n",
              "0  A group of kids is playing in a yard and an ol...             neutral   \n",
              "\n",
              "   relatedness_score_En corr_entailment_labelAB_En corr_entailment_labelBA_En  \\\n",
              "0                   3.3                        NaN                        NaN   \n",
              "\n",
              "                        sentence_A_Ja                          sentence_B_Ja  \\\n",
              "0  戸外で遊んでいる男の子は一人もおらず、微笑んでいる男性は一人もいない  子供たちのグループが庭で遊んでいて、後ろの方には年を取った男性が立っている   \n",
              "\n",
              "  entailment_label_Ja  relatedness_score_Ja                   image_ID  \\\n",
              "0       contradiction                   2.3  3155657768_b83a7831e5.jpg   \n",
              "\n",
              "                                    original_caption        semtag_short  \\\n",
              "0  The children are playing outdoors , while a ma...  Negation#Numerical   \n",
              "\n",
              "                                         semtag_long  \n",
              "0  Numerical;人;名詞,接尾,助数詞,*#Negation;ない;助動詞,*,*,*#...  "
            ],
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              "\n",
              "  <div id=\"df-7eecc221-85b6-4bf4-8c43-b17aadbcd676\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>pair_ID</th>\n",
              "      <th>data</th>\n",
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              "      <th>sentence_B_En</th>\n",
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              "      <th>relatedness_score_En</th>\n",
              "      <th>corr_entailment_labelAB_En</th>\n",
              "      <th>corr_entailment_labelBA_En</th>\n",
              "      <th>sentence_A_Ja</th>\n",
              "      <th>sentence_B_Ja</th>\n",
              "      <th>entailment_label_Ja</th>\n",
              "      <th>relatedness_score_Ja</th>\n",
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              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>test</td>\n",
              "      <td>There is no boy playing outdoors and there is ...</td>\n",
              "      <td>A group of kids is playing in a yard and an ol...</td>\n",
              "      <td>neutral</td>\n",
              "      <td>3.3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>戸外で遊んでいる男の子は一人もおらず、微笑んでいる男性は一人もいない</td>\n",
              "      <td>子供たちのグループが庭で遊んでいて、後ろの方には年を取った男性が立っている</td>\n",
              "      <td>contradiction</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3155657768_b83a7831e5.jpg</td>\n",
              "      <td>The children are playing outdoors , while a ma...</td>\n",
              "      <td>Negation#Numerical</td>\n",
              "      <td>Numerical;人;名詞,接尾,助数詞,*#Negation;ない;助動詞,*,*,*#...</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-7eecc221-85b6-4bf4-8c43-b17aadbcd676');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df = pd.read_csv(\"https://github.com/verypluming/JSICK/raw/main/jsick/test.tsv\", sep=\"\\t\")\n",
        "df.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxMYLgOC4ZPv",
        "outputId": "5c366682-e2c3-47d6-e9d3-0083c4c8d40b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4927, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3yZJO5h4ZPv"
      },
      "source": [
        "## Encode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaV0t7yC4ZPv",
        "outputId": "a96f64df-4169-4dd4-8c55-7315d9b5b98b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(TensorShape([4927, 512]), TensorShape([4927, 512]))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "sentence1_embs = embed(df[\"sentence_A_Ja\"])\n",
        "sentence2_embs = embed(df[\"sentence_B_Ja\"])\n",
        "sentence1_embs.shape, sentence2_embs.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqWgdrWd4ZPw"
      },
      "source": [
        "## Correlation Score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "yUbTfCbK4ZPw",
        "outputId": "a90555e1-4ffe-4084-8b53-7af134663170"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8003486058678017"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "from scipy.spatial.distance import cosine\n",
        "from scipy.stats import spearmanr\n",
        "df[\"similarity\"] = [1 - cosine(s1, s2) for s1, s2 in zip(sentence1_embs, sentence2_embs)]\n",
        "spearmanr(df[\"similarity\"], df[\"relatedness_score_Ja\"])[0]"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "venv",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.11"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "02c2702a58ea2fe60fd5f26dd152a70e7993d77024040a4f035d0ea16923b730"
      }
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}