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        "<a href=\"https://colab.research.google.com/github/TurkuNLP/textual-data-analysis-course/blob/main/tda2023_xlmr_stransformer_2019data_solved.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "!pip3 install transformers datasets"
      ]
    },
    {
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      "source": [
        "import transformers\n",
        "import datasets"
      ],
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        "import datetime\n",
        "dset=datasets.load_dataset(\"json\",\n",
        "                           data_files={\"en\":\"http://dl.turkunlp.org/TKO_8964_2023/news-en-2019.jsonl\", \n",
        "                                       \"fi\":\"http://dl.turkunlp.org/TKO_8964_2023/news-fi-2019.jsonl\"})\n",
        "\n",
        "## Some simple sorting and filtering operations can be done directly on the dataset\n",
        "dset=dset.sort(\"timestamp\")\n",
        "# dset=dset.filter(lambda example: example[\"timestamp\"]>datetime.datetime(2019,12,1,0,0))\n",
        "print(dset)\n"
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            "WARNING:datasets.builder:Using custom data configuration default-00f060784ba4a285\n"
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            ],
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          "metadata": {}
        },
        {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating en split: 0 examples [00:00, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "e86c700c03fc40d4a9d2dc29cb0b7121"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating fi split: 0 examples [00:00, ? examples/s]"
            ],
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              "model_id": "5751124fbf6b428f8b67cf16979b093e"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-00f060784ba4a285/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
          ]
        },
        {
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              "model_id": "611e01f1a6e4439189f6911ed00d6ed4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    en: Dataset({\n",
            "        features: ['summary', 'tags', 'text', 'timestamp', 'title', 'url'],\n",
            "        num_rows: 2481\n",
            "    })\n",
            "    fi: Dataset({\n",
            "        features: ['summary', 'tags', 'text', 'timestamp', 'title', 'url'],\n",
            "        num_rows: 24015\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for elem in dset[\"en\"].select(range(10)):\n",
        "    print(elem[\"timestamp\"],elem[\"title\"])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tCgtM5sgnRV",
        "outputId": "e21d12b7-e3ac-4894-eda4-fe2db43f3224"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2018-12-26 10:22:56 Holiday return traffic peaking Wednesday\n",
            "2018-12-26 12:30:00 Watch: Reindeer alert app makes Lapland roads safer for drivers\n",
            "2018-12-26 15:00:00 Finland loosens name law, but most still prefer classics\n",
            "2018-12-27 04:45:10 Thursday's papers: Fireworks ban, ski lift sabotage and most desirable employers\n",
            "2018-12-27 10:12:56 Thousands of overseas applicants vie for seasonal jobs in Lapland\n",
            "2018-12-27 11:30:00 Hike benefits to tackle lengthening food queues, academic warns\n",
            "2018-12-27 13:29:39 THL: Microbes pose no threat to winter swimmers\n",
            "2018-12-27 15:00:00 Suspect in Helsinki child murder had history of violent crime, records show\n",
            "2018-12-27 15:45:00 Finland's Border Guards brace for seasonal influx of Russian tourists\n",
            "2018-12-27 16:45:00 Finland's most spectacular gingerbread house is from Kuopio\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Computing transformer embeddings\n",
        "\n",
        "* The easiest way is to use the `pipeline` abstraction\n",
        "* You give it a model, a task, and a hint on where to run the model (CPU vs GPU)\n",
        "* The task of extracting the embeddings is called `feature-extraction`\n",
        "* truncation: dealing with too long inputs\n",
        "* batch_size: batching for efficiency"
      ],
      "metadata": {
        "id": "SCisqxz4dsAS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "\n",
        "## This loads the basic XLM-R model\n",
        "#p=transformers.pipeline(task=\"feature-extraction\",model=\"xlm-roberta-base\",return_tensors=True,device=0)\n",
        "\n",
        "## This loads the appropriate SentenceTransformer model\n",
        "p=transformers.pipeline(task=\"feature-extraction\",model=\"sentence-transformers/paraphrase-xlm-r-multilingual-v1\", tokenizer=\"sentence-transformers/paraphrase-xlm-r-multilingual-v1\",return_tensors=True,device=0)\n",
        "\n",
        "## This call I copied from here:\n",
        "## https://huggingface.co/docs/transformers/main_classes/pipelines#pipeline-batching\n",
        "fi_embedded=p(transformers.pipelines.pt_utils.KeyDataset(dset[\"fi\"], \"title\"), batch_size=64, truncation=\"only_first\")\n",
        "en_embedded=p(transformers.pipelines.pt_utils.KeyDataset(dset[\"en\"], \"title\"), batch_size=64, truncation=\"only_first\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Runtime efficiency and device placement\n",
        "\n",
        "* The pipeline processes examples in batches and yields the results\n",
        "* The results are by default placed on the same device as the model, in our case the GPU\n",
        "* We do don't want to have these stick around for too long\n",
        "* Below is an attempt at efficient processing:\n",
        "  1. Pool the token embeddings right away by averaging\n",
        "  2. Move the result out from the GPU right away\n",
        "* We end up with all data embedded, and nicely in a list"
      ],
      "metadata": {
        "id": "M8eKJV0bmPXv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch \n",
        "fi_embedded_pooled=[torch.mean(elem,axis=1) for elem in fi_embedded]\n",
        "en_embedded_pooled=[torch.mean(elem,axis=1) for elem in en_embedded]"
      ],
      "metadata": {
        "id": "4c2vxtMaVQtP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Every result is a 768-long embedding of one input document\n",
        "* We can cash in a doc_count-by-768 tensor by stacking these in torch"
      ],
      "metadata": {
        "id": "CGhRjyGanEoS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(fi_embedded_pooled[0].shape)\n",
        "print(len(fi_embedded_pooled))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRpXwvuDXVYa",
        "outputId": "be70d13c-9271-44fa-a066-5e30b3d9a2a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 768])\n",
            "24015\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_fi=torch.vstack(fi_embedded_pooled)\n",
        "all_en=torch.vstack(en_embedded_pooled)"
      ],
      "metadata": {
        "id": "RzENUHfpYjYD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cosine similarity\n",
        "\n",
        "* We can now compare all embeddings together\n",
        "* There is a handy tool for that in sklearn\n",
        "* But for that to work, we need to turn the torch tensors into numpy ndarrays (`.numpy()` method)"
      ],
      "metadata": {
        "id": "TkjM-bkTnTwg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn.metrics\n",
        "M=sklearn.metrics.pairwise.cosine_similarity(all_en.numpy(),all_fi.numpy())\n",
        "\n"
      ],
      "metadata": {
        "id": "rfytCvvHYZyt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* To get the id of the most similar document to any other document, argsort is just the right tool"
      ],
      "metadata": {
        "id": "pIP3W4eQ0_0h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy\n",
        "print(M.shape)\n",
        "aligned=numpy.argsort(-M,axis=-1) # -M so that we get a descending sort"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkEbwnhWY9NX",
        "outputId": "38e63da7-a218-4a82-d884-4b2ac2d0d453"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2481, 24015)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   We need to manage our expectations here\n",
        "*   Not every piece of news will have another one corresponding to it\n",
        "* Definitely not if we compare the English and Finnish data, since there is so much less in the English data\n",
        "* So we should look at pairs in their descending order of similarity\n",
        "* ...to get the best matches first\n",
        "* There is a (somewhat mind-bending) way to achieve this in NumPy, but we can go about this with a simple loop here\n",
        "* when comparing within one language (i.e. you run all embeddings against each other), the first hit is naturally the example itself\n",
        "* (if you do not understand Finnish, you can use DeepL to convince yourself that this indeed works)\n"
      ],
      "metadata": {
        "id": "BkQPwcePn3Vg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pairs=[]\n",
        "for i in range(M.shape[0]): #M.shape[0] is the number of rows / input documents\n",
        "    j=aligned[i,0] # 0 is the best hit now that we have two sets of sentences\n",
        "    score=M[i,j]\n",
        "    pairs.append((i,j,score))\n",
        "pairs.sort(key=lambda ijs:ijs[2],reverse=True) #Sort the matches in descending order of their similarity"
      ],
      "metadata": {
        "id": "6dHNTJ6oarHQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i,j,score in pairs[:30]:\n",
        "    print(score)\n",
        "    print(dset[\"en\"][\"title\"][i])\n",
        "    print(dset[\"fi\"][\"title\"][j])\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Grq7spaZNVa",
        "outputId": "c1ea8869-befe-4d57-befe-8d26d238bc58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9419267\n",
            "Presidents Niinistö and Macron meet in Paris\n",
            "Presidentti Niinistö ja Macron tapaavat tänään Pariisissa\n",
            "\n",
            "0.92731106\n",
            "Survey: Only six percent of young people believe climate change will be controlled\n",
            "Kysely: Vain kuusi prosenttia nuorista uskoo, että ilmastonmuutoksen hillinnässä onnistutaan\n",
            "\n",
            "0.9134449\n",
            "Foreign Minister Haavisto to negotiate on Sudan for EU\n",
            "Ulkoministeri Pekka Haavisto neuvottelemaan Sudanin tilanteesta EU:n puolesta\n",
            "\n",
            "0.9126774\n",
            "Stora Enso cutting up to 400 jobs in Oulu\n",
            "Stora Enso aloittaa yt-neuvottelut Oulussa – paperikone suljetaan, 400 työpaikkaa uhattuna\n",
            "\n",
            "0.89801073\n",
            "Supo: Large numbers of foreign intelligence staff in Finland; Russia and China particularly active\n",
            "Supo: Suomessa on suuri määrä ulkomaisten tiedustelupalveluiden henkilöstöä – erityisesti Venäjä ja Kiina aktiivisia\n",
            "\n",
            "0.8855576\n",
            "35,000 more employed than one year ago\n",
            "Tilastokeskus: Työllisiä 35 000 enemmän kuin vuosi sitten\n",
            "\n",
            "0.88420236\n",
            "Putin to visit Helsinki next Wednesday\n",
            "Putin Suomeen vierailulle ensi viikon keskiviikkona\n",
            "\n",
            "0.88360524\n",
            "Finland ranked second on Press Freedom Index, behind Norway\n",
            "Suomi nousi lehdistönvapausindeksissä toiselle sijalle, edellä vielä Norja\n",
            "\n",
            "0.8785144\n",
            "Report: Women's pensions one-fifth smaller than men's\n",
            "Naisten eläkkeet viidenneksen pienempiä kuin miesten eläkkeet – ero pienentynyt vain vähän\n",
            "\n",
            "0.87405926\n",
            "President Niinistö to visit Ukraine next month\n",
            "Presidentti Niinistö jatkaa vierailua Ukrainassa, tapaa uuden pääministerin\n",
            "\n",
            "0.8731855\n",
            "Rinne and Medvedev meet in Moscow\n",
            "Pääministeri Rinne tapaa tänään Moskovassa Medvedevin\n",
            "\n",
            "0.8723653\n",
            "Police suspect tax fraud by China-linked Tampere media firm\n",
            "Poliisi epäilee törkeitä veropetoksia Kiinan valtioon kytkeytyneessä Tampereelta johdetussa mediatoimistossa\n",
            "\n",
            "0.8711804\n",
            "Foreign Ministry: Two children to be repatriated from al-Hol camp\n",
            "Ulkoministeriö: Kaksi lasta kotiutetaan al-Holin leiriltä\n",
            "\n",
            "0.8673078\n",
            "Helsinki Airport expands again: EU lends 150mn for new main entrance and train station tunnel\n",
            "Helsinki-Vantaa laajenee jälleen: Uusi pääsisäänkäynti ja tunneli juna-asemalle – Euroopan investointipankki lainasi 150 miljoonaa\n",
            "\n",
            "0.8649314\n",
            "S&P Global Ratings maintains Finland’s AA+ rating\n",
            "Luottoluokittaja S&P Global Ratings pitää Suomen luottoluokituksen ennallaan\n",
            "\n",
            "0.8633113\n",
            "Police: Reporting of sex offences up 16%\n",
            "Poliisi: Seksuaalirikosten ilmoitusmäärä kasvoi 16 prosenttia\n",
            "\n",
            "0.86306816\n",
            "SDP chair Antti Rinne returns to work after 2 months' sick leave\n",
            "SDP:n puheenjohtaja Antti Rinne palaa perjantaina töihin ja tv-kameroiden eteen parin kuukauden sairauslomalta\n",
            "\n",
            "0.8613889\n",
            "Survey: Finland divided on benefit of Swedish language skills\n",
            "Kysely: Kysymys ruotsin kielen hyödyllisyydestä jakaa suomalaiset jyrkästi kahtia – neljäsosa sanoo, ettei osaa kieltä lainkaan\n",
            "\n",
            "0.8607599\n",
            "Lidl pulls minced beef over salmonella fears\n",
            "Lidl vetää naudan jauhelihaa myynnistä salmonellaepäilyn takia\n",
            "\n",
            "0.8597845\n",
            "Police: Two adults stabbed, three children injured in Helsinki attack\n",
            "Poliisi: Mies puukotti kahta aikuista Helsingissä, aiheutti vammoja myös kolmelle lapselle – epäilty kateissa\n",
            "\n",
            "0.8580245\n",
            "Police: reporting of sex offences up 16 percent\n",
            "Poliisi: Seksuaalirikosten ilmoitusmäärä kasvoi 16 prosenttia\n",
            "\n",
            "0.8578434\n",
            "Finland holds steady in global competitiveness ranking\n",
            "Suomi säilyttänyt asemansa laajassa kilpailukykyvertailussa\n",
            "\n",
            "0.8514439\n",
            "Yle Areena tops Finland’s most valued internet brand list\n",
            "Yle Areena on kaikilla mittareilla Suomen arvostetuin verkkobrändi\n",
            "\n",
            "0.8437754\n",
            "Breezy year boosts wind power output by nearly 30%\n",
            "Uutissuomalainen: Hyvä tuulivuosi kasvattanut tuulivoiman tuotantoa lähes 30 prosentilla\n",
            "\n",
            "0.84332\n",
            "Helsinki to hike wages for home care and social workers\n",
            "Helsinki korottaa kotihoidon ja sosiaalityöntekijöiden palkkoja: \"Siitä on pitkä aika, kun vastaavaa on ollut\"\n",
            "\n",
            "0.84068465\n",
            "Finland: Still the happiest country in the world (says UN report)\n",
            "Suomi on maailman onnellisin maa jo toista vuotta peräkkäin, nyt selvällä erolla muihin\n",
            "\n",
            "0.8405732\n",
            "Niinistö and Putin meet in Russia\n",
            "Niinistö tapaa ensi viikolla Putinin Pietarissa\n",
            "\n",
            "0.8405061\n",
            "CMI removes links to MP Hussein al-Taee from its website\n",
            "Verkkouutiset: CMI poisti maininnan Hussein al-Taeesta\n",
            "\n",
            "0.83486134\n",
            "MP al-Taee’s sick leave to continue through May\n",
            "Al-Taeen sairausloma jatkuu – poissa ainakin kesäkuun loppuun\n",
            "\n",
            "0.83370197\n",
            "Darude to represent Finland at the Eurovision Song Contest\n",
            "Darude edustaa Suomea Euroviisuissa – \"Tiedän, että sinne pitää mennä voittamaan\"\n",
            "\n"
          ]
        }
      ]
    }
  ]
}