squad_as_sent_classification_solved.ipynb

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        "<a href=\"https://colab.research.google.com/github/TurkuNLP/textual-data-analysis-course/blob/main/squad_as_sent_classification_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": [
        "# QA as sentence classification\n",
        "\n",
        "## Task 1:\n",
        "\n",
        "* Familiarize yourself with the whole notebook and what is going on there\n",
        "* At the very bottom there is a place where you can simply run a trained model\n",
        "* Print some interesting correct and incorrect classifications (e.g. cases where the model failed to answer \"yes\" - false negatives, or cases where it overpredicted a \"yes\" - false positives)\n",
        "* Do these make sense? Would you do better?\n",
        "\n",
        "## Task 2:\n",
        "\n",
        "* Try to see if you can run the training yourself on the dataset\n",
        "* AND add precision and recall to the reported metrics so you can better see what happens to the positive class\n",
        "* Make sure you save the models etc\n",
        "\n",
        "## Task 3:\n",
        "\n",
        "* You will notice that the basic model is heavily skewed towards predicting the (more common) negative class\n",
        "* The correct way to address this is by introducing class weights into training\n",
        "* Try, for example, to train a model with a 0.9 weight on the positive class and 0.1 on the negative, does anything change? And what if you turn the weights around, and give the positive class a 0.1 weight, will the model ever predict any?\n",
        "* Google will help you with how to add class weights into the training process (it involves overriding the compute_loss() method of the trainer)\n",
        "\n",
        "## Task 4:\n",
        "\n",
        "* Those of you who want, once you make it to the point you can train your own model, you can do it on Finnish\n",
        "* The Finnish QA data is pointed to below"
      ]
    },
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip3 install evaluate transformers datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "de99d32a-14a3-4d94-9443-999f4ea19f24",
      "metadata": {
        "id": "de99d32a-14a3-4d94-9443-999f4ea19f24"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "import datasets\n",
        "import evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "03288d5e-b428-46d2-9011-b211b96ada56",
      "metadata": {
        "id": "03288d5e-b428-46d2-9011-b211b96ada56"
      },
      "source": [
        "# SQuAD v2\n",
        "\n",
        "* First, we will load and explore the SQuAD v2 dataset\n",
        "* It is also quite important to read through the SQuAD v2 paper to understand the finer points of the data\n",
        "* `squad_v2` is the original English dataset\n",
        "* `TurkuNLP/squad_v2_fi` is a machine-translated version produced by TurkuNLP"
      ]
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec5561bb69ff473db197ef6c8f6eafa9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset squad_v2/squad_v2 to /root/.cache/huggingface/datasets/squad_v2/squad_v2/2.0.0/09187c73c1b837c95d9a249cd97c2c3f1cebada06efe667b4427714b27639b1d...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "255653dac7ab41e68ee7a19d3a426409"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/9.55M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "739a5931d79b405994e76c6ae8113c0d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/801k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af4865bdcbed443a87788cef5592dd7c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74b5fb19e79f4b3da97cfc67d4b57e00"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/130319 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2796efeede043bba26772c68b192d1b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/11873 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebf973e95caf49f7802df579c784901f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset squad_v2 downloaded and prepared to /root/.cache/huggingface/datasets/squad_v2/squad_v2/2.0.0/09187c73c1b837c95d9a249cd97c2c3f1cebada06efe667b4427714b27639b1d. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b3191526bca449896bbb8751fb93491"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#squad_dataset_full=datasets.load_dataset(\"TurkuNLP/squad_v2_fi\")\n",
        "squad_dataset_full=datasets.load_dataset(\"squad_v2\")\n",
        "squad_dataset_full=squad_dataset_full.shuffle()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7933312-dde0-4dba-868e-24b3956e2fb4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7933312-dde0-4dba-868e-24b3956e2fb4",
        "outputId": "56e6dc49-7dbe-45f2-f5f1-dcadb17efd79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': '573057502461fd1900a9cd7a', 'title': 'Swaziland', 'context': 'The Reed Dance today is not an ancient ceremony but a development of the old \"umchwasho\" custom. In \"umchwasho\", all young girls were placed in a female age-regiment. If any girl became pregnant outside of marriage, her family paid a fine of one cow to the local chief. After a number of years, when the girls had reached a marriageable age, they would perform labour service for the Queen Mother, ending with dancing and feasting. The country was under the chastity rite of \"umchwasho\" until 19 August 2005.', 'question': 'From where did the Reed Dance originate?', 'answers': {'text': ['the old \"umchwasho\" custom'], 'answer_start': [69]}}\n",
            "\n",
            "{'id': '5ad31566604f3c001a3fdb44', 'title': 'Cardinal_(Catholicism)', 'context': \"When in choir dress, a Latin-rite cardinal wears scarlet garments — the blood-like red symbolizes a cardinal's willingness to die for his faith. Excluding the rochet — which is always white — the scarlet garments include the cassock, mozzetta, and biretta (over the usual scarlet zucchetto). The biretta of a cardinal is distinctive not merely for its scarlet color, but also for the fact that it does not have a pompon or tassel on the top as do the birettas of other prelates. Until the 1460s, it was customary for cardinals to wear a violet or blue cape unless granted the privilege of wearing red when acting on papal business. His normal-wear cassock is black but has scarlet piping and a scarlet fascia (sash). Occasionally, a cardinal wears a scarlet ferraiolo which is a cape worn over the shoulders, tied at the neck in a bow by narrow strips of cloth in the front, without any 'trim' or piping on it. It is because of the scarlet color of cardinals' vesture that the bird of the same name has become known as such.[citation needed]\", 'question': 'What does the blood like red not symbolize for a cardinal?', 'answers': {'text': [], 'answer_start': []}}\n",
            "\n",
            "{'id': '5ad3dcf2604f3c001a3ff429', 'title': 'Tristan_da_Cunha', 'context': 'Executive authority is vested in the Queen, who is represented in the territory by the Governor of Saint Helena. As the Governor resides permanently in Saint Helena, an Administrator is appointed to represent the Governor in the islands. The Administrator is a career civil servant in the Foreign Office and is selected by London. Since 1998, each Administrator has served a single, three-year term (which begins in September, upon arrival of the supply ship from Cape Town.) The Administrator acts as the local head of government, and takes advice from the Tristan da Cunha Island Council. Alex Mitham was appointed Tristan da Cunha’s 22nd Administrator and arrived, with his wife Hasene, to take over from Sean Burns in September 2013. The Island Council is made up of eight elected and three appointed members, who serve a 3-year term which begins in February (or March).', 'question': 'When did Sean Burns arrive to takeover from Alex Mitham?', 'answers': {'text': [], 'answer_start': []}}\n",
            "\n",
            "{'id': '5a7d0ad970df9f001a874f2b', 'title': 'Neolithic', 'context': \"The Neolithic 2 (PPNB) began around 8,800 BCE according to the ASPRO chronology in the Levant (Jericho, Israel). As with the PPNA dates, there are two versions from the same laboratories noted above. This system of terminology, however, is not convenient for southeast Anatolia and settlements of the middle Anatolia basin. This era was before the Mesolithic era.[citation needed] A settlement of 3,000 inhabitants was found in the outskirts of Amman, Jordan. Considered to be one of the largest prehistoric settlements in the Near East, called 'Ain Ghazal, it was continuously inhabited from approximately 7,250 – 5,000 B.\", 'question': 'When was Amman continuously inhabited?', 'answers': {'text': [], 'answer_start': []}}\n",
            "\n",
            "{'id': '570bd2ec6b8089140040fa6a', 'title': 'ASCII', 'context': 'When a Teletype 33 ASR equipped with the automatic paper tape reader received a Control-S (XOFF, an abbreviation for transmit off), it caused the tape reader to stop; receiving Control-Q (XON, \"transmit on\") caused the tape reader to resume. This technique became adopted by several early computer operating systems as a \"handshaking\" signal warning a sender to stop transmission because of impending overflow; it persists to this day in many systems as a manual output control technique. On some systems Control-S retains its meaning but Control-Q is replaced by a second Control-S to resume output. The 33 ASR also could be configured to employ Control-R (DC2) and Control-T (DC4) to start and stop the tape punch; on some units equipped with this function, the corresponding control character lettering on the keycap above the letter was TAPE and TAPE respectively.', 'question': 'What was the warning signal called that warned the sender that there was impending overflow?', 'answers': {'text': ['handshaking'], 'answer_start': [322]}}\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for item in squad_dataset_full[\"train\"].select(range(5)):\n",
        "    print(item)\n",
        "    print()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "865f6078-8159-4ad3-86d1-c14635a19f37",
      "metadata": {
        "id": "865f6078-8159-4ad3-86d1-c14635a19f37"
      },
      "source": [
        "# QA as a classification task\n",
        "\n",
        "* As a warm-up problem, let us cast QA as a classification problem\n",
        "* The task is: does the string at hand contain the answer to the question?\n",
        "* This is not how you would do QA but it is a good start, and an interesting problem in its own right\n",
        "* Maybe we can give ourselves some slack, and go sentence-by-sentence\n",
        "* So, we need to split the context into sentences, and then assemble a yes/no classification data\n",
        "* But how do we split the text into sentences?\n",
        "* Udpipe is an old, but trusty library to do this\n",
        "* Trained models: https://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_25_models\n",
        "* Go familiarize yourself with that tool and models!\n",
        "* For Finnish, make sure you use the TDT model (Made in Turku), which is the best\n",
        "* For English, the EWT (English Web Treebank) model is a good choice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "557eeba8-e8ca-4785-b298-bc1427c76e9f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "557eeba8-e8ca-4785-b298-bc1427c76e9f",
        "outputId": "181cb5fb-313b-4095-aeba-6af3e8da1bcc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting ufal.udpipe\n",
            "  Downloading ufal.udpipe-1.2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (848 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m848.5/848.5 KB\u001b[0m \u001b[31m39.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ufal.udpipe\n",
            "Successfully installed ufal.udpipe-1.2.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip3 install ufal.udpipe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2418d68d-1f81-49f4-8fc6-28e52a1a3fdc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2418d68d-1f81-49f4-8fc6-28e52a1a3fdc",
        "outputId": "5866c532-3906-4d45-c137-ac45baa55130"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-31 18:25:37--  https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3131/english-ewt-ud-2.5-191206.udpipe?sequence=17&isAllowed=y\n",
            "Resolving lindat.mff.cuni.cz (lindat.mff.cuni.cz)... 195.113.20.140\n",
            "Connecting to lindat.mff.cuni.cz (lindat.mff.cuni.cz)|195.113.20.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16309608 (16M) [application/octet-stream]\n",
            "Saving to: ‘english-ewt.udpipe’\n",
            "\n",
            "english-ewt.udpipe  100%[===================>]  15.55M  6.60MB/s    in 2.4s    \n",
            "\n",
            "2023-01-31 18:25:42 (6.60 MB/s) - ‘english-ewt.udpipe’ saved [16309608/16309608]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O english-ewt.udpipe 'https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3131/english-ewt-ud-2.5-191206.udpipe?sequence=17&isAllowed=y'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2ca8dc89-999d-4df0-80e0-79cb0115f015",
      "metadata": {
        "id": "2ca8dc89-999d-4df0-80e0-79cb0115f015"
      },
      "source": [
        "# Running UDPipe\n",
        "\n",
        "* This is surprisingly easy, once you figure it out\n",
        "* The documentation (from which I figured it out) is here:\n",
        "  * https://ufal.mff.cuni.cz/udpipe/1/users-manual#run_udpipe_tokenizer\n",
        "  * https://ufal.mff.cuni.cz/udpipe/1/api-reference#pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e475b270-d2e2-4347-865c-d1fc87929bc8",
      "metadata": {
        "id": "e475b270-d2e2-4347-865c-d1fc87929bc8"
      },
      "outputs": [],
      "source": [
        "import ufal.udpipe\n",
        "udpipemodel=ufal.udpipe.Model.load(\"english-ewt.udpipe\")\n",
        "tokenizer=ufal.udpipe.Pipeline(udpipemodel,\"tokenizer=ranges\",\"none\",\"none\",\"conllu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e6f55a8-7e62-4081-bae3-deb88db4c8c8",
      "metadata": {
        "id": "9e6f55a8-7e62-4081-bae3-deb88db4c8c8",
        "outputId": "c0b08cc4-4a7c-4cf5-9e59-dd10f06464a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "# newdoc\n",
            "# newpar\n",
            "# sent_id = 1\n",
            "# text = I have a dog.\n",
            "1\tI\t_\t_\t_\t_\t_\t_\t_\tTokenRange=0:1\n",
            "2\thave\t_\t_\t_\t_\t_\t_\t_\tTokenRange=2:6\n",
            "3\ta\t_\t_\t_\t_\t_\t_\t_\tTokenRange=7:8\n",
            "4\tdog\t_\t_\t_\t_\t_\t_\t_\tSpaceAfter=No|TokenRange=9:12\n",
            "5\t.\t_\t_\t_\t_\t_\t_\t_\tTokenRange=12:13\n",
            "\n",
            "# sent_id = 2\n",
            "# text = The dog is cute.\n",
            "1\tThe\t_\t_\t_\t_\t_\t_\t_\tTokenRange=14:17\n",
            "2\tdog\t_\t_\t_\t_\t_\t_\t_\tTokenRange=18:21\n",
            "3\tis\t_\t_\t_\t_\t_\t_\t_\tTokenRange=22:24\n",
            "4\tcute\t_\t_\t_\t_\t_\t_\t_\tSpaceAfter=No|TokenRange=25:29\n",
            "5\t.\t_\t_\t_\t_\t_\t_\t_\tTokenRange=29:30\n",
            "\n",
            "# sent_id = 3\n",
            "# text = And brown at that.\n",
            "1\tAnd\t_\t_\t_\t_\t_\t_\t_\tTokenRange=31:34\n",
            "2\tbrown\t_\t_\t_\t_\t_\t_\t_\tTokenRange=35:40\n",
            "3\tat\t_\t_\t_\t_\t_\t_\t_\tTokenRange=41:43\n",
            "4\tthat\t_\t_\t_\t_\t_\t_\t_\tSpaceAfter=No|TokenRange=44:48\n",
            "5\t.\t_\t_\t_\t_\t_\t_\t_\tSpacesAfter=\\n|TokenRange=48:49\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(tokenizer.process(\"I have a dog. The dog is cute. And brown at that.\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "11cf8162-c521-43de-a5d2-e41b9856905a",
      "metadata": {
        "id": "11cf8162-c521-43de-a5d2-e41b9856905a"
      },
      "source": [
        "# Find the sentence with the answer\n",
        "* Let's take a shortcut and simply use the `text=` field to find sentences which have the answer\n",
        "* Of course this might lead to spurious hits where the string is repeated in several sentences, but acts as the correct answer only in one\n",
        "* The right solution would be to use the `TokenRange` data to find the correct sentence that has the right answer\n",
        "* I leave that to you as an extra exercise if you wish\n",
        "* All you need is to parse the output of UDPipe, takes an extra for-loop, really"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "678d1651-c46a-4cd1-8ada-68dadeb09068",
      "metadata": {
        "id": "678d1651-c46a-4cd1-8ada-68dadeb09068"
      },
      "outputs": [],
      "source": [
        "import tqdm\n",
        "def get_sentences(parsed):\n",
        "    # gather the text= lines with the sentences\n",
        "    sents=[line.replace(\"# text = \",\"\") for line in parsed.split(\"\\n\") if line.startswith(\"# text = \")]\n",
        "    return sents\n",
        "\n",
        "def qa_to_binary(example,tokenizer):\n",
        "    context=example[\"context\"]\n",
        "    question=example[\"question\"]\n",
        "    sentences=get_sentences(tokenizer.process(context))\n",
        "    result=[]\n",
        "    #compare every sentence with every answer, when you find an answer, you have an example\n",
        "    for sent in sentences:\n",
        "        for answer in example[\"answers\"][\"text\"]:\n",
        "            if answer in sent:\n",
        "                result.append({\"question\":question,\"context\":sent,\"label\":1,\"answer\":answer})\n",
        "                break\n",
        "        else: #we found no answers, so this sentence is then 0\n",
        "            result.append({\"question\":question,\"context\":sent,\"label\":0,\"answer\":None})\n",
        "    return result\n",
        "\n",
        "train_processed=[]\n",
        "for item in tqdm.tqdm(squad_dataset_full[\"train\"].select(range(50000))):\n",
        "    train_processed.extend(qa_to_binary(item,tokenizer))\n",
        "test_processed=[]\n",
        "for item in tqdm.tqdm(squad_dataset_full[\"validation\"].select(range(2000))):\n",
        "    test_processed.extend(qa_to_binary(item,tokenizer))\n",
        "\n",
        "                      \n",
        "\n",
        "        "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5f60f800-4de1-44d0-897b-c33fa9906c6f",
      "metadata": {
        "id": "5f60f800-4de1-44d0-897b-c33fa9906c6f"
      },
      "source": [
        "# Save the data\n",
        "\n",
        "* Now the usual trick of saving the data in a nice, processed form as jsonl\n",
        "* And then load as a HF Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32d3267d-962c-4073-8ac0-466cae821f4d",
      "metadata": {
        "id": "32d3267d-962c-4073-8ac0-466cae821f4d"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "with open(\"squad_2_binarized_train.json\",\"wt\") as f:\n",
        "    json.dump(train_processed,f,ensure_ascii=False,indent=2)\n",
        "with open(\"squad_2_binarized_test.json\",\"wt\") as f:\n",
        "    json.dump(test_processed,f,ensure_ascii=False,indent=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a09276bc-3444-4e97-aa2c-15df6979ae2a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249,
          "referenced_widgets": [
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        },
        "id": "a09276bc-3444-4e97-aa2c-15df6979ae2a",
        "outputId": "ea51464e-37ae-43ef-c297-4e89fc384e3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-b2a88c8518fe60b6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-b2a88c8518fe60b6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7efd0767c2c24acea9701b972e73e4aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/74.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c638f053be1c45a3baf7bca38f26aacf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/3.12M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "049aafad62734e87a5a1a356f4942576"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "754b84687e9745c2bebbd93f7da7c50a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6352da05d5a489e8b3ae61cb8829db2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f5837aa424514b2abcc41c97ebc5fd6d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-b2a88c8518fe60b6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c3942abab47e4f919dfaaf5721a2270f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#dset=datasets.load_dataset(\"json\",data_files={\"train\":\"squad_2_binarized_train.json\",\"test\":\"squad_2_binarized_test.json\"},download_mode=\"force_redownload\")\n",
        "dset=datasets.load_dataset(\"json\",data_files={\"train\":\"http://dl.turkunlp.org/TKO_8964_2023/squad_2_binarized_train.json\",\"test\":\"http://dl.turkunlp.org/TKO_8964_2023/squad_2_binarized_test.json\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3d6cf37-abac-4ca8-92a7-e77fd507e3dd",
      "metadata": {
        "id": "f3d6cf37-abac-4ca8-92a7-e77fd507e3dd"
      },
      "outputs": [],
      "source": [
        "dset=dset.shuffle()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f19e1760-3ee4-4235-b8a1-70d1ac70e9a0",
      "metadata": {
        "id": "f19e1760-3ee4-4235-b8a1-70d1ac70e9a0"
      },
      "source": [
        "# Train the classifier\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6e58c148-5e9e-456d-928f-8acc94ebe323",
      "metadata": {
        "colab": {
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        },
        "id": "6e58c148-5e9e-456d-928f-8acc94ebe323",
        "outputId": "e7379b77-437e-45a2-dfad-4deb2bf806d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d2e927cf40cd4a4591583553d351721b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9baef81840d54250bddef3656b4b2ccf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9159e5521ea4116b5dcd1c2e4f0f554"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d7dc1a003f0433ea86b8be175441d70"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "MODEL = 'bert-base-cased'\n",
        "tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7e6d8bd2-863b-4bb7-9df9-9a90b9acaa3b",
      "metadata": {
        "id": "7e6d8bd2-863b-4bb7-9df9-9a90b9acaa3b"
      },
      "source": [
        "* The tokenizer now needs a pair of texts (the question and the context)\n",
        "* For BERT, it will actually then do the right thing with token type IDs\n",
        "* The two texts are passed as `text` and `text_pair` (for some confusing naming)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "35ac7cd3-77e6-4e14-b631-2966f75b62ba",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
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        "id": "35ac7cd3-77e6-4e14-b631-2966f75b62ba",
        "outputId": "21d3dfd2-d79a-4b94-af52-de163e7bf05c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/259266 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "317d3d88fcd54ac0a183b5ceda7593d1"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10703 [00:00<?, ?ex/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbe3f5e9aaca4158825aab7217310df1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def tokenize(example):\n",
        "    return tokenizer(text=example[\"question\"],text_pair=example[\"context\"], truncation=True)\n",
        "\n",
        "dataset = dset.map(tokenize)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "079909a4-ea61-4f84-97e8-d271fad8ef74",
      "metadata": {
        "id": "079909a4-ea61-4f84-97e8-d271fad8ef74"
      },
      "outputs": [],
      "source": [
        "trainer_args = transformers.TrainingArguments(\n",
        "    output_dir='checkpoints',\n",
        "    evaluation_strategy='steps',\n",
        "    logging_strategy='steps',\n",
        "    load_best_model_at_end=True,\n",
        "    eval_steps=250,\n",
        "    logging_steps=250,\n",
        "    learning_rate=0.000015,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    max_steps=5000,\n",
        "    save_steps=250,\n",
        "    save_total_limit=6 #I will have an early stopping with patience 5, so max 6 checkpoints sounds reasonable\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1711a9c2-7f7d-4583-b7d9-53265f73b6cb",
      "metadata": {
        "id": "1711a9c2-7f7d-4583-b7d9-53265f73b6cb"
      },
      "source": [
        "# How to evaluate?\n",
        "\n",
        "* The data is imbalanced\n",
        "* The positive class is much rarer than the negative class\n",
        "* High accuracy does not tell the whole story\n",
        "* It will be good to report also precision and recall, so we can gauge what happens to the class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc0bf723-bc29-4ec5-8a96-30fb44faf94f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        "outputId": "98c14f9c-6db0-4123-ad32-e91a94cf5b82"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/7.55k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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      "source": [
        "accuracy = evaluate.load('accuracy')\n",
        "precision = evaluate.load('precision')\n",
        "recall = evaluate.load('recall')\n",
        "\n",
        "\n",
        "def compute_accuracy(outputs_and_labels):\n",
        "    outputs, labels = outputs_and_labels\n",
        "    predictions = outputs.argmax(axis=-1) # pick index of \"winning\" label\n",
        "    acc=accuracy.compute(predictions=predictions, references=labels)\n",
        "    prec=precision.compute(predictions=predictions, references=labels)\n",
        "    rec=recall.compute(predictions=predictions, references=labels)\n",
        "    return dict(**acc,**prec,**rec)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26e5c19e-245e-4aa6-9485-1f46e3c7ead1",
      "metadata": {
        "id": "26e5c19e-245e-4aa6-9485-1f46e3c7ead1"
      },
      "source": [
        "* Let's also add EarlyStopping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fb80b16d-5fe1-4119-88bd-6a24873de874",
      "metadata": {
        "id": "fb80b16d-5fe1-4119-88bd-6a24873de874"
      },
      "outputs": [],
      "source": [
        "estopping_cback=transformers.EarlyStoppingCallback(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e7a2e347-175d-41ce-b1db-e3aadb04e635",
      "metadata": {
        "id": "e7a2e347-175d-41ce-b1db-e3aadb04e635"
      },
      "source": [
        "# Class imbalance\n",
        "\n",
        "* One way to deal with imbalanced data is to give the minority class higher weight during training\n",
        "* I.e. the loss is weighted by class-dependent weights\n",
        "* A way to implement this can be copied straight from the HF documentation\n",
        "* You can invent some weights, or maybe you can use the formula/code from e.g. here https://scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a558e927-076f-4dfe-8e10-54bd7e536f88",
      "metadata": {
        "id": "a558e927-076f-4dfe-8e10-54bd7e536f88"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "class WeightTrainer(transformers.Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.get(\"labels\")\n",
        "        # forward pass\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.get(\"logits\")\n",
        "        # compute custom loss (suppose one has 3 labels with different weights)\n",
        "        loss_fct = torch.nn.CrossEntropyLoss(weight=torch.tensor([0.1, 0.9],device=logits.device))\n",
        "        loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))\n",
        "        return (loss, outputs) if return_outputs else loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ecec50b5-f1c7-4449-bbce-943a9e5d7b0f",
      "metadata": {
        "id": "ecec50b5-f1c7-4449-bbce-943a9e5d7b0f"
      },
      "outputs": [],
      "source": [
        "# A little housekeeping\n",
        "# to help with various memory leaks\n",
        "\n",
        "import gc\n",
        "try:\n",
        "    del trainer\n",
        "except:\n",
        "    pass\n",
        "try:\n",
        "    del model\n",
        "except:\n",
        "    pass\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "805c5133-9c05-41e9-a73f-90f97a034d25",
      "metadata": {
        "id": "805c5133-9c05-41e9-a73f-90f97a034d25"
      },
      "outputs": [],
      "source": [
        "#This is something I needed on CSC's puhti\n",
        "#why, I have no idea\n",
        "try:\n",
        "    import mlflow\n",
        "    mlflow.end_run()\n",
        "    mlflow.start_run()\n",
        "except:\n",
        "    pass\n",
        "\n",
        "model = transformers.AutoModelForSequenceClassification.from_pretrained(\n",
        "    MODEL,\n",
        "    num_labels=2\n",
        " )\n",
        "\n",
        "trainer = WeightTrainer(\n",
        "    model=model,\n",
        "    args=trainer_args,\n",
        "    train_dataset=dataset['train'],\n",
        "    eval_dataset=dataset['test'],\n",
        "    compute_metrics=compute_accuracy,\n",
        "    tokenizer=tokenizer,\n",
        "    callbacks=[estopping_cback], \n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "514b26ef-9de9-4c3b-9684-f5cbe454670c",
      "metadata": {
        "id": "514b26ef-9de9-4c3b-9684-f5cbe454670c"
      },
      "outputs": [],
      "source": [
        "trainer.save_model(\"english-binarized-weighted.model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Trained models\n",
        "\n",
        "* I left the trained models for you here, in case you want them\n",
        "* http://dl.turkunlp.org/TKO_8964_2023/"
      ],
      "metadata": {
        "id": "5aZCEuj_HhtI"
      },
      "id": "5aZCEuj_HhtI"
    },
    {
      "cell_type": "markdown",
      "id": "1d8d2a1e-1143-48fa-b996-e5401512a25c",
      "metadata": {
        "id": "1d8d2a1e-1143-48fa-b996-e5401512a25c"
      },
      "source": [
        "# Test the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b654c239-9add-4228-b459-8e4446dda795",
      "metadata": {
        "id": "b654c239-9add-4228-b459-8e4446dda795"
      },
      "outputs": [],
      "source": [
        "model=transformers.AutoModelForSequenceClassification.from_pretrained(\"english-binarized-weighted.model\")\n",
        "tokenizer=transformers.AutoTokenizer.from_pretrained(\"english-binarized-weighted.model\",truncation=True,padding=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5a65a0f3-ce12-4ea7-85ae-f00bcea8ecd1",
      "metadata": {
        "id": "5a65a0f3-ce12-4ea7-85ae-f00bcea8ecd1"
      },
      "outputs": [],
      "source": [
        "dset=datasets.load_from_disk(\"shuffled_binarized_squad2_dataset\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "02f9cd75-93a4-48e7-a686-3460ef06390c",
      "metadata": {
        "id": "02f9cd75-93a4-48e7-a686-3460ef06390c"
      },
      "outputs": [],
      "source": [
        "pipe=transformers.TextClassificationPipeline(model=model,tokenizer=tokenizer,device=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9fadaf07-5a45-4443-968b-0b08c5a68c72",
      "metadata": {
        "id": "9fadaf07-5a45-4443-968b-0b08c5a68c72"
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      "outputs": [],
      "source": [
        "# Hmm, this is not the right way to go about it, perhaps, but the KeyPairDataset\n",
        "# I could not get working, even though that would be the right way to do it\n",
        "# {\"text\":[listoftexts],\"text_pair\":[listoftexts]} should have worked according to docs,\n",
        "# but did not in reality :D\n",
        "res=pipe([{\"text\":e[\"question\"],\"text_pair\":e[\"context\"]} for e in dset[\"test\"].select(range(100))])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d58c99a4-3778-4b7e-8ca7-80d597168493",
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        "id": "d58c99a4-3778-4b7e-8ca7-80d597168493",
        "outputId": "660e9365-f639-4e33-b564-6e2581893d77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "What type of hypersensitivity is associated with allergies?\n",
            "Type IV reactions are involved in many autoimmune and infectious diseases, but may also involve contact dermatitis (poison ivy).\n",
            "\n",
            "Who first described dynamic equilibrium?\n",
            "Simple experiments showed that Galileo's understanding of the equivalence of constant velocity and rest were correct.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Let's print out some interesting misclassifications\n",
        "\n",
        "for prediction,datapoint in zip(res,dset[\"test\"].select(range(100))):\n",
        "    if prediction[\"label\"]==\"LABEL_0\" and datapoint[\"label\"]==1:\n",
        "        print(datapoint[\"question\"])\n",
        "        print(datapoint[\"context\"])\n",
        "        print()"
      ]
    }
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