doc/gpt2_finetuning.ipynb

{
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    {
      "cell_type": "markdown",
      "id": "6DaRDd7taXxn",
      "metadata": {
        "id": "6DaRDd7taXxn"
      },
      "source": [
        "# Finetuning GPT2 on ELI5-Data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3xtg3KBca9FT",
      "metadata": {
        "id": "3xtg3KBca9FT"
      },
      "source": [
        "Downloading dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "ZYr4QjdrVkJO",
      "metadata": {
        "colab": {
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        "outputId": "3e08b3f8-1a53-44f0-8b91-cca9e5879599"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-1.18.0 frozenlist-1.3.0 fsspec-2022.1.0 multidict-5.2.0 xxhash-2.0.2 yarl-1.7.2\n"
          ]
        }
      ],
      "source": [
        "! pip install transformers\n",
        "! pip install yake\n",
        "! pip install datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2Tk6ROiSa6rZ",
      "metadata": {
        "id": "2Tk6ROiSa6rZ"
      },
      "source": [
        "Importing dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "5aa19482",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aa19482",
        "outputId": "0dfc8317-4df3-4d8b-c240-2ae513713177"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "PyTorch version: 1.10.0+cu111\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import re\n",
        "import random\n",
        "\n",
        "from datasets import list_datasets, load_dataset\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForPreTraining, \\\n",
        "                         TrainingArguments, Trainer, pipeline, AutoModelForTokenClassification\n",
        "\n",
        "import yake\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "print(f\"PyTorch version: {torch.__version__}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "yEZOiiW9a25Y",
      "metadata": {
        "id": "yEZOiiW9a25Y"
      },
      "source": [
        "Verifying GPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "87Xo8_-0V9in",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87Xo8_-0V9in",
        "outputId": "9773199f-20dd-4c75-ebe3-5596d5ecd477"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sat Jan 22 19:00:04 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.46       Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   35C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "32e755f3",
      "metadata": {
        "id": "32e755f3"
      },
      "source": [
        "# Configurations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "49c81013",
      "metadata": {
        "id": "49c81013"
      },
      "outputs": [],
      "source": [
        "MODEL           = 'gpt2' # Used model for finetuning (huggingface.co/gpt2)\n",
        "\n",
        "UNFREEZE_LAST_N = 6 # The last N layers to unfreeze for training\n",
        "\n",
        "SPECIAL_TOKENS  = { \"bos_token\": \"<|BOS|>\",\n",
        "                    \"eos_token\": \"<|EOS|>\",\n",
        "                    \"unk_token\": \"<|UNK|>\",                    \n",
        "                    \"pad_token\": \"<|PAD|>\",\n",
        "                    \"sep_token\": \"<|SEP|>\"}\n",
        "                    \n",
        "MAXLEN          = 768 # Max len of generated text\n",
        "\n",
        "TRAIN_SIZE      = 0.8 # Train split\n",
        "\n",
        "USE_APEX        = True\n",
        "\n",
        "#lowers Batch size in case of little ram\n",
        "if USE_APEX:\n",
        "    TRAIN_BATCHSIZE = 4\n",
        "    BATCH_UPDATE    = 16\n",
        "else:\n",
        "    TRAIN_BATCHSIZE = 2\n",
        "    BATCH_UPDATE    = 32\n",
        "\n",
        "EPOCHS          = 4\n",
        "LR              = 5e-4\n",
        "EPS             = 1e-8\n",
        "WARMUP_STEPS    = 1e2\n",
        "\n",
        "SEED            = 2022"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5xe4ovjwb5cw",
      "metadata": {
        "id": "5xe4ovjwb5cw"
      },
      "source": [
        "initiate seeds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "d014ca16",
      "metadata": {
        "id": "d014ca16"
      },
      "outputs": [],
      "source": [
        "random.seed(SEED)\n",
        "os.environ['PYTHONHASHSEED'] = str(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed(SEED)\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = True"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "68224df2",
      "metadata": {
        "id": "68224df2"
      },
      "source": [
        "# Load ELI5 Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2da3b5b3",
      "metadata": {
        "id": "2da3b5b3"
      },
      "source": [
        "Available at huggingface: https://huggingface.co/datasets/eli5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "02b540bb",
      "metadata": {
        "colab": {
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          "height": 0,
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        },
        "id": "02b540bb",
        "outputId": "47583f4f-e5cd-4a43-c090-6b2e2cd88e83"
      },
      "outputs": [
        {
          "data": {
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              "model_id": "7a6fb2c20220454ea3cbebdae58a92c7",
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              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset eli5/LFQA_reddit (download: 6.03 MiB, generated: 1.26 GiB, post-processed: Unknown size, total: 1.26 GiB) to /root/.cache/huggingface/datasets/eli5/LFQA_reddit/1.0.0/17574e5502a10f41bbd17beba83e22475b499fa62caa1384a3d093fc856fe6fa...\n"
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        {
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          },
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        {
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        {
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        {
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        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset eli5 downloaded and prepared to /root/.cache/huggingface/datasets/eli5/LFQA_reddit/1.0.0/17574e5502a10f41bbd17beba83e22475b499fa62caa1384a3d093fc856fe6fa. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
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              "model_id": "271ad444b7e74676924ce8e48cbd375c",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "eli5_dataset = load_dataset('eli5')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ERYsjWVFcaty",
      "metadata": {
        "id": "ERYsjWVFcaty"
      },
      "source": [
        "Print data sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "ceaf3b34",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ceaf3b34",
        "outputId": "3bdf8eba-3c62-4f58-b1ad-0cbc10cb96bd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'answers': {'a_id': ['cyi9d1m',\n",
              "   'cyi990h',\n",
              "   'cyi9do0',\n",
              "   'cyiger5',\n",
              "   'cyilcl7',\n",
              "   'cyiidqq',\n",
              "   'cyi9i76',\n",
              "   'cyiic6t',\n",
              "   'cyibpiq',\n",
              "   'cyikhsv'],\n",
              "  'score': [148, 57, 45, 19, 19, 6, 6, 4, 2, 2],\n",
              "  'text': ['I like to think that leather clothing is rather more durable and easy to fix than an Italian tailored suit.\\n\\nSo in lack of industrial infrastructure (lack of shops, sewing machines, fabric production etc.) Simple and durable clothing would become common.\\n\\nAs for makeup, i guess black makeup (or motor oil, dirt, grit) is easy to pull off than perfectly clean face.',\n",
              "   'To a lot of of observers, the nearest present-day mirror that we have to a dystopian and post-apocalyptic society is the part of our own current society that prefers to dress and appear that way. Biker ~~games~~ **gangs** (edit: used an incorrect worm) are a good example.\\n\\nPeople who dress and wear a whole lot of dark make-up or tattoos are generally seen as tougher and grittier than Joe Average, maybe better armed and organized as well. They and their sideways society or undersociety would likely be better suited to survive an apocalypse when the veneer of civility started wearing thin. So they\\'d take that preference in clothing and make-up with them into their \"new order\" in whatever seedier and grittier universe resulted from the change, to the point where that appearance would become the norm.',\n",
              "   \"Inventing a new, believable dress code isn't easy, so when you're depicting a society that's radically different from our current one, an easy solution is picking the dress code of people who are already perceived as antisocial or non-conforming, like punks and bikers.\",\n",
              "   'All of the previous 17 answers do shed a great deal of light on this question.  I would also add that there is symbolism in this kind of aesthetic.  These are the less conventional types of fashion choices and they can be used to convey non-conformity.  And so, if you wish to signal a failure of conventional society, and the rise of a more anarchistic, uncontrolled type of human existence, you can use non-conformist fashion to indicate that there is no longer any remaining social convention to which people might conform.\\n\\nBut that is an artistic choice, to send a message to audiences.  If we were to try to predict what a post-apocalyptic society would actually look like, possibly it would just look like a lot of badly dressed people who no longer had convenient access to clothing stores.  There would probably be no such thing as fashion or any prevailing aesthetic, in a world in which people are struggling to survive at all.  It would also be true that if you did feel like wearing eye make-up and studded leather clothing, and you were fortunate enough to have access to those things, chances are there would be nobody to stop you.  Your mother is not going to tell you \"take that off, you look ridiculous\".',\n",
              "   \"I think it goes back to Mad Max and George Miller's vision of the future.  Mad Max came out in 1979, are there any dystopian biker gangs in movies that predate that?\\n\\nFake edit:  So I googled and came up with [this page of Australian dystopia's](_URL_1_) and it mentions [The Cars that Ate Paris](_URL_0_) by Peter Weir from 1974 which hugely influenced George Miller; he even referenced one of the film's iconic cars in the latest Mad Max.  \\n\\nSo now the question is, does anything predate this movie or is Pete Weir the one responsible?\",\n",
              "   'Some of these answers lean in the right direction. I can add a little conserving spikes and studs. Aside from the \"look\" it gives, studs and spikes can be a bit more practical... Dogs wear spiked collars around their necks to prevent another dog from grabbing hold of it, protecting it from attack. The same idea transferred to the punk aesthetic, with spiked bracelets, collars and such. A well-studded jacket might also turn a fist or knife in a fight. A spiked wrist can\\'t be grabbed. Skinny jeans... Well, those aren\\'t actually that practical. \\n\\n Anecdotally, that was the intent. It took the toughness of leather and denim with an extra layer of protection for rumbling gangs.  Granted, most of what you see in that look nowadays is toned down and mall-ready. \\n\\nIn a post-apocalyptic society, would you be afraid of it was roving gangs of nerds? Probably not. But if the streets were turned over and ran by the shadier element; biker gangs, punks, thugs, etc... You\\'re actually worried about society.',\n",
              "   \"There is a lot of human skin lying around that needs to be used, and people don't get a lot of sleep so it isn't eye makeup, they just are really, really tired.\",\n",
              "   'I think Hollywood\\'s fascination with the black leather dominatrix look for post-Armageddon societies is kind of silly.  Most likely actual PAS would have a shortage of cloth after a while, and denizens of the post-industrial world would look (and smell) a lot like heavily armed hobos.  While it makes an exciting film, PAS\\'s that have a lot of motor vehicles is also nonsensical and irrational.  PAS\\'s would depend mostly on bicycles, horses, two-wheeled and four-wheeled carts and so on for transportation.  Within a few years fixed ammunition would become difficult to obtain, transportation would become animal-powered and materials scavengers would wreck most complicated machinery (no longer functional because of a lack of electricity) looking for items they could use.  Hunger would be wide-spread and society would slowly devolve into warlordism and feudalism.  Organizations like renegade military units and outlaw motorcycle clubs would create little Somalia-like mini-states.  Regular people, people who either were not heavily armed to begin with, or who were not allowed to join an outlaw tribe would simply become subjects of the warlords.  Have you ever seen the Kevin Costner movie, \"The Postman\"?   Like that.  Junked cars blocking highways everywhere.  No gasoline.  Home-made clothing, moccasins, patches upon patches upon patches.  TEOTWAWKI--the End of the World as we know it.  (I loved that song, BTW.)',\n",
              "   'Tactical choices, to some degree. In post apocalyptia, there will be fighting. People slaughtering each other for what precious few resources remain. You need so avoid being killed. To that end, leather is a great choice. Its tough and durable while also light and non-restricting, is good against the elements and tends to give an intimidating appearance.\\n\\ndark makeup in post apocalypic movies tends to be skewed, messily applied at best. Theres less opportunity to wash, limited resources, often no good mirrors. Dark makeup will help keep sun glare to a minimum, but its not going to stay neat and proper for very long when youre rolling around on the ground and sweating.',\n",
              "   'A lot of those movies were made in the 70s when punk was all the rage.  Its just like how everyone in Star Trek: The Next Generation dresses l like its 1987.  I guess fashion really is cyclical.']},\n",
              " 'answers_urls': {'url': ['https://www.youtube.com/watch?v=xKrZ8eTcdK4',\n",
              "   'http://www.bfi.org.uk/news-opinion/news-bfi/lists/10-great-dystopian-australian-road-movies']},\n",
              " 'document': '',\n",
              " 'q_id': '3z0jp8',\n",
              " 'selftext': '[removed]',\n",
              " 'selftext_urls': {'url': []},\n",
              " 'subreddit': 'explainlikeimfive',\n",
              " 'title': 'How did studded leather and heavy eye makeup come to be the Hollywood dress code for dystopian, post-apocalyptic societies?',\n",
              " 'title_urls': {'url': []}}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# multiple answers and their score/rating/upvotes\n",
        "# Answers are stored in descending order inside 'text', only first (best) answer will be considered\n",
        "eli5_dataset[\"test_eli5\"][1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dNyKnOf3c8Rs",
      "metadata": {
        "id": "dNyKnOf3c8Rs"
      },
      "source": [
        "Filtering redundant datapoints:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "6af9a4b7",
      "metadata": {
        "id": "6af9a4b7"
      },
      "outputs": [],
      "source": [
        "df = pd.DataFrame(data=eli5_dataset[\"test_eli5\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "5a5fec09",
      "metadata": {
        "id": "5a5fec09"
      },
      "outputs": [],
      "source": [
        "df = df.drop(columns=[\"q_id\", \"selftext\", \"document\", \"subreddit\", \"title_urls\", \"selftext_urls\", \"answers_urls\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "407ba416",
      "metadata": {
        "id": "407ba416"
      },
      "outputs": [],
      "source": [
        "# filtering out the answers\n",
        "for i in df.index:\n",
        "    df.answers[i] = df.answers[i][\"text\"][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "3318335c",
      "metadata": {
        "id": "3318335c"
      },
      "outputs": [],
      "source": [
        "# merging question and answers to one\n",
        "df[\"full_text\"] = df[\"title\"] + \" \" + df[\"answers\"]\n",
        "df[\"keyword\"] = \"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "L0KFqvFnm9_t",
      "metadata": {
        "id": "L0KFqvFnm9_t"
      },
      "outputs": [],
      "source": [
        "# For development, only 10k posts will be considered\n",
        "df = df[:10000]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2dcd5184",
      "metadata": {
        "id": "2dcd5184"
      },
      "source": [
        "# Extracting Keywords"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "uXZndtHvMWBq",
      "metadata": {
        "id": "uXZndtHvMWBq"
      },
      "source": [
        "Reference for yake from SeemsPhishy code:\n",
        "\n",
        "```\n",
        "! pip install yake\n",
        "import yake\n",
        "import sys\n",
        "def keywords_yake(text, language = \"en\", max_ngram_size = 2, numOfKeywords = 1):\n",
        "\n",
        "    custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, top=numOfKeywords)\n",
        "    keywords = custom_kw_extractor.extract_keywords(text)\n",
        "    return keywords\n",
        "\n",
        "for i in df.index:\n",
        "    df[\"keyword\"][i] = keywords_yake(df.full_text[i])[0][0]\n",
        "    sys.stdout.write(\"\\rExtracting keyword: %i\" % i)\n",
        "    sys.stdout.flush()\n",
        "df\n",
        "\n",
        "data = dict()\n",
        "for id in df.index:\n",
        "    data[id] = [df[\"title\"][id], df[\"answers\"][id], [df[\"keyword\"][id]]]\n",
        "```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xhx9sT8DdaE4",
      "metadata": {
        "id": "xhx9sT8DdaE4"
      },
      "source": [
        "## Keyword extraction approach A: Yake"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "upnq3rAQdg-x",
      "metadata": {
        "id": "upnq3rAQdg-x"
      },
      "outputs": [],
      "source": [
        "def keywords_yake(text, language = \"en\", max_ngram_size = 1, numOfKeywords = 1):\n",
        "\n",
        "    custom_kw_extractor = yake.KeywordExtractor(lan=language, n=max_ngram_size, top=numOfKeywords)\n",
        "    keywords = custom_kw_extractor.extract_keywords(text)\n",
        "    return keywords"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2wen95eM9KgX",
      "metadata": {
        "id": "2wen95eM9KgX"
      },
      "source": [
        "## Keyword extraction approach B: Tf-idf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "941X7jIBF0qh",
      "metadata": {
        "id": "941X7jIBF0qh"
      },
      "outputs": [],
      "source": [
        "def sort_coo(coo_matrix):\n",
        "    \"\"\"Sort a dict with highest score\"\"\"\n",
        "    tuples = zip(coo_matrix.col, coo_matrix.data)\n",
        "    return sorted(tuples, key=lambda x: (x[1], x[0]), reverse=True)\n",
        "\n",
        "def extract_topn_from_vector(feature_names, sorted_items, topn=10):\n",
        "    \"\"\"get the feature names and tf-idf score of top n items\"\"\"\n",
        "    \n",
        "    #use only topn items from vector\n",
        "    sorted_items = sorted_items[:topn]\n",
        "\n",
        "    score_vals = []\n",
        "    feature_vals = []\n",
        "    \n",
        "    # word index and corresponding tf-idf score\n",
        "    for idx, score in sorted_items:\n",
        "        \n",
        "        #keep track of feature name and its corresponding score\n",
        "        score_vals.append(round(score, 3))\n",
        "        feature_vals.append(feature_names[idx])\n",
        "\n",
        "    #create a tuples of feature, score\n",
        "    results= {}\n",
        "    for idx in range(len(feature_vals)):\n",
        "        results[feature_vals[idx]]=score_vals[idx]\n",
        "    \n",
        "    return results\n",
        "\n",
        "def get_keywords(vectorizer, feature_names, doc):\n",
        "    \"\"\"Return top k keywords from a doc using TF-IDF method\"\"\"\n",
        "\n",
        "    #generate tf-idf for the given document\n",
        "    tf_idf_vector = vectorizer.transform([doc])\n",
        "    \n",
        "    #sort the tf-idf vectors by descending order of scores\n",
        "    sorted_items=sort_coo(tf_idf_vector.tocoo())\n",
        "\n",
        "    #extract only TOP_K_KEYWORDS\n",
        "    keywords=extract_topn_from_vector(feature_names,sorted_items,3)\n",
        "    \n",
        "    return list(keywords.keys())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "OPU3Gv-seL1_",
      "metadata": {
        "id": "OPU3Gv-seL1_"
      },
      "source": [
        "# Preprocessing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "o21X6rh79Mth",
      "metadata": {
        "id": "o21X6rh79Mth"
      },
      "outputs": [],
      "source": [
        "#PUNCTUATION = \"\"\"!\"#$%&'()*+,-.:;<=>?@[]^_`{|}~\"\"\"\n",
        "\n",
        "def clean_text(text, training = False):\n",
        "  text = re.sub(\"\\(_URL_[0-9]+_\\)\",\" \",text)\n",
        "  text = \"\".join([c for c in text if c not in \"[]<>*_\"])\n",
        "  text = text.replace(\"\\n\", \" \") #remove newlines\n",
        "\n",
        "  if training is not True:  \n",
        "    text = text.lower()\n",
        "    text = re.sub(\"\\s+\",\" \", text) #remove whitespace and newlines\n",
        "    \n",
        "  return text"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cWahd2UZKvxH",
      "metadata": {
        "id": "cWahd2UZKvxH"
      },
      "source": [
        "Clean the answers for model finetuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "_U6PZvVrK0oL",
      "metadata": {
        "id": "_U6PZvVrK0oL"
      },
      "outputs": [],
      "source": [
        "for i in df.index:\n",
        "  df[\"answers\"][i] = clean_text(df[\"answers\"][i], training = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "ZD6biuKzgV97",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZD6biuKzgV97",
        "outputId": "77678e96-d760-4aee-c2d3-076a0656b07a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0       I think it's because, at that moment, it's bas...\n",
              "1       I like to think that leather clothing is rathe...\n",
              "2       Shrubs and trees are both specifically woody p...\n",
              "3       Moving air = lower pressure. The greater the d...\n",
              "4       It's kind of like a \"3 strikes and you're out\"...\n",
              "                              ...                        \n",
              "9995    The charts aren't based on YouTube views - the...\n",
              "9996    I perceive certain animals as food, chickens, ...\n",
              "9997    Relevant xkcd   DVDs and Blu Rays come in Keep...\n",
              "9998    In an alphabetic script one symbol represents ...\n",
              "9999    The new exoskeleton stays soft as it is formed...\n",
              "Name: answers, Length: 10000, dtype: object"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"answers\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "BveOEmAuLUT_",
      "metadata": {
        "id": "BveOEmAuLUT_"
      },
      "source": [
        "Clean the full text for keyword extraction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "DWMyXPMHLYkN",
      "metadata": {
        "id": "DWMyXPMHLYkN"
      },
      "outputs": [],
      "source": [
        "for i in df.index:\n",
        "  df[\"full_text\"][i] = clean_text(df[\"full_text\"][i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "u20HFwFhDbzm",
      "metadata": {
        "id": "u20HFwFhDbzm"
      },
      "outputs": [],
      "source": [
        "vectorizer = TfidfVectorizer(stop_words=set(stopwords.words(\"english\")), smooth_idf=True, use_idf=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "4k6qQ5-VEhyM",
      "metadata": {
        "id": "4k6qQ5-VEhyM"
      },
      "outputs": [],
      "source": [
        "corpora = df[\"full_text\"].to_list()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "Pk8J4eoEE22R",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pk8J4eoEE22R",
        "outputId": "dd9537fc-f298-41d9-e2c8-c9bfbd1bcca7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<10000x36210 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 506220 stored elements in Compressed Sparse Row format>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vectorizer.fit_transform(corpora[:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "JbFrTb3-FcjF",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbFrTb3-FcjF",
        "outputId": "ab97c113-3748-4b52-e5a7-c70a42fc1d2c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ],
      "source": [
        "feature_names = vectorizer.get_feature_names()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "-7kieGjme3sB",
      "metadata": {
        "id": "-7kieGjme3sB"
      },
      "source": [
        "Tf-idf keyword extraction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "fplU3PBRFfoe",
      "metadata": {
        "id": "fplU3PBRFfoe"
      },
      "outputs": [],
      "source": [
        "for i in df.index:\n",
        "    #tfidf_df = {}\n",
        "    #tfidf_df['full_text'] = corpora[i]\n",
        "    df['keyword'][i] = get_keywords(vectorizer, feature_names, corpora[i])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "AojpXF-he8l1",
      "metadata": {
        "id": "AojpXF-he8l1"
      },
      "source": [
        "Yake keyword extraction"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "0k7hWndIlO1o",
      "metadata": {
        "id": "0k7hWndIlO1o"
      },
      "outputs": [],
      "source": [
        "df[\"yake_keyword\"] = \"\"\n",
        "for i in df.index:\n",
        "  df[\"yake_keyword\"][i] = [keyword[0] for keyword in keywords_yake(df.full_text[i], numOfKeywords=3)] # get the keyword only without probability\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "q44YZwRNM3HO",
      "metadata": {
        "id": "q44YZwRNM3HO"
      },
      "source": [
        "# Named entity recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "qdyS_dD3Q1K8",
      "metadata": {
        "id": "qdyS_dD3Q1K8"
      },
      "source": [
        "Below is the code that was tried for NER. As it turned out, the dataset doesn't provide enough entities. The initial thought was to extract entities that could further be used for finetuning, so that the model eventually learns to incorporate new unseen entities.\n",
        "\n",
        "```\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\")\n",
        "\n",
        "nlp = pipeline(\"ner\", model=model, tokenizer=tokenizer)\n",
        "example = \"My name is Wolfgang and I live in Berlin\"\n",
        "\n",
        "ner_results = nlp(example)\n",
        "print(ner_results)\n",
        "df[\"ner\"] = \"\"\n",
        "for i in df.index:\n",
        "  ner_results = nlp(df[\"full_text\"][i])\n",
        "  df[\"ner\"][i] = [entity[\"word\"] for entity in ner_results]\n",
        "#ner doesn't work too well...\n",
        "nlp(\"why can a big music name (e.g. keith urban) get into the billboard top 100 easily with 1,000,000 song views, but a new/independent artist needs 50,000,000 or even 100,000,000 song views to make it into the billboard hot 100? the charts aren't based on youtube views - they're still mostly based on sales & radio play. most unsigned artists on youtube aren't even getting counted at all because the charts *are run by the record industry*.\")\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "74e350a8",
      "metadata": {
        "id": "74e350a8"
      },
      "source": [
        "# Datasets processing for finetuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "bc2ead7f",
      "metadata": {
        "id": "bc2ead7f"
      },
      "outputs": [],
      "source": [
        "class myDataset(Dataset):\n",
        "\n",
        "    def __init__(self, data, tokenizer, randomize=True):\n",
        "\n",
        "        title, text, keywords = [], [], [] # df = title \tanswers \tfull_text \tkeyword_A keyword_B\n",
        "        for k, v in data.items():\n",
        "            title.append(v[0])\n",
        "            text.append(v[1])\n",
        "            keywords.append(v[2])\n",
        "\n",
        "        self.randomize = randomize\n",
        "        self.tokenizer = tokenizer \n",
        "        self.title     = title\n",
        "        self.text      = text\n",
        "        self.keywords  = keywords  \n",
        "\n",
        "\n",
        "    @staticmethod\n",
        "    def join_keywords(keywords, randomize=True):\n",
        "        N = len(keywords)\n",
        "\n",
        "        #random sampling and shuffle\n",
        "        if randomize: \n",
        "            M = random.choice(range(N+1))\n",
        "            keywords = keywords[:M]\n",
        "            random.shuffle(keywords)\n",
        "\n",
        "        return ','.join(keywords)\n",
        "\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.text)\n",
        "\n",
        "    \n",
        "    def __getitem__(self, i):\n",
        "        keywords = self.keywords[i].copy() #list of keywords [k1, k2, k3]\n",
        "        kw = self.join_keywords(keywords, self.randomize)\n",
        "        \n",
        "        # training data consists of keywords, questions and the corresponding answers\n",
        "        input = SPECIAL_TOKENS['bos_token'] + \\\n",
        "                SPECIAL_TOKENS['sep_token'] + kw + SPECIAL_TOKENS['sep_token'] + \\\n",
        "                self.title[i] + \": \" + self.text[i] + SPECIAL_TOKENS['eos_token']\n",
        "\n",
        "        encodings_dict = tokenizer(input,                                   \n",
        "                                   truncation=True, \n",
        "                                   max_length=MAXLEN, \n",
        "                                   padding=\"max_length\")   \n",
        "        \n",
        "        input_ids = encodings_dict['input_ids']\n",
        "        attention_mask = encodings_dict['attention_mask']\n",
        "        \n",
        "        return {'label': torch.tensor(input_ids),\n",
        "                'input_ids': torch.tensor(input_ids), \n",
        "                'attention_mask': torch.tensor(attention_mask)}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "f5526594",
      "metadata": {
        "id": "f5526594"
      },
      "outputs": [],
      "source": [
        "def split_data(data, S=TRAIN_SIZE):\n",
        "    # Shuffle ids\n",
        "    ids = list(data.keys())\n",
        "    random.shuffle(ids)\n",
        "\n",
        "    # Split into training and validation sets    \n",
        "    train_size = int(S * len(data))\n",
        "\n",
        "    train_ids = ids[:train_size]\n",
        "    val_ids = ids[train_size:]\n",
        "\n",
        "    train_data = dict()\n",
        "    for id in train_ids:\n",
        "        train_data[id] = data[id]\n",
        "\n",
        "    val_data = dict()\n",
        "    for id in val_ids:\n",
        "        val_data[id] = data[id]\n",
        "\n",
        "    return train_data, val_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ac2c49c",
      "metadata": {
        "id": "3ac2c49c"
      },
      "source": [
        "# Loading Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "a3d31f4f",
      "metadata": {
        "id": "a3d31f4f"
      },
      "outputs": [],
      "source": [
        "def get_tokenizer(special_tokens=None):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(MODEL) # uses the gpt2 tokenizer\n",
        "\n",
        "    if special_tokens:\n",
        "        tokenizer.add_special_tokens(special_tokens)\n",
        "        print(\"Special tokens added\")\n",
        "    return tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "372998bb",
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        "colab": {
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              "Downloading:   0%|          | 0.00/665 [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ce99234632214cd0986c9fc95cb7c626",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/0.99M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5eb67ea5c8d46a588b78d3b0538fd3b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "454f7a81e9df47dcbf7634fdbab4bb7e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Special tokens added\n"
          ]
        }
      ],
      "source": [
        "tokenizer = get_tokenizer(special_tokens=SPECIAL_TOKENS)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8bc9d4a7",
      "metadata": {
        "id": "8bc9d4a7"
      },
      "source": [
        "# Loading Config and Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "7f2aed0d",
      "metadata": {
        "id": "7f2aed0d"
      },
      "outputs": [],
      "source": [
        "def get_model(tokenizer, special_tokens=None, load_model_path=None):\n",
        "\n",
        "    #GPT2LMHeadModel\n",
        "    if special_tokens:\n",
        "        config = AutoConfig.from_pretrained(MODEL, \n",
        "                                            bos_token_id=tokenizer.bos_token_id,\n",
        "                                            eos_token_id=tokenizer.eos_token_id,\n",
        "                                            sep_token_id=tokenizer.sep_token_id,\n",
        "                                            pad_token_id=tokenizer.pad_token_id,\n",
        "                                            output_hidden_states=False)\n",
        "    else: \n",
        "        config = AutoConfig.from_pretrained(MODEL,                                     \n",
        "                                            pad_token_id=tokenizer.eos_token_id,\n",
        "                                            output_hidden_states=False)    \n",
        "\n",
        "\n",
        "    model = AutoModelForPreTraining.from_pretrained(MODEL, config=config)\n",
        "\n",
        "    if special_tokens:\n",
        "        #Special tokens added, model needs to be resized accordingly\n",
        "        model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "    if load_model_path:\n",
        "        model.load_state_dict(torch.load(load_model_path))\n",
        "\n",
        "    model.cuda()\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "127f6299",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "bb2933d2038c4f2b8e6f789175f4a755",
            "22b8ce8b0f694302b1789e998fe610e8",
            "85f697b1da6a43cfbde7f62ba53b60ac",
            "372784f38cca4b59b9eef5f48845fc17",
            "811e61ddf77e4176863ab3472af14acb",
            "3c4746b6f8cc464c9d1af69bf925f033",
            "13171d86a8864d32a15bebdbb79020f6",
            "0e1104954a4f434f9a55d4aae4d6c3e3",
            "8fec7cce82934001a25fe9ae2183ccfb",
            "b7736e3fdaac48d3ab6237ff294f6cf8",
            "6073e557c6874816a1b6068e5a51503b"
          ]
        },
        "id": "127f6299",
        "outputId": "de9f2100-5265-4876-9539-37b41fc0fc34"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb2933d2038c4f2b8e6f789175f4a755",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/523M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = get_model(tokenizer, \n",
        "                  special_tokens=SPECIAL_TOKENS,\n",
        "                #   load_model_path='pytorch_model.bin'\n",
        "                 )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2978f22",
      "metadata": {
        "id": "d2978f22"
      },
      "outputs": [],
      "source": [
        "# - Freeze selective layers:\n",
        "# - Freeze all layers except last n:\n",
        "for parameter in model.parameters():\n",
        "    parameter.requires_grad = False\n",
        "\n",
        "for i, m in enumerate(model.transformer.h):        \n",
        "    #Only un-freeze the last n transformer blocks\n",
        "    if i+1 > 12 - UNFREEZE_LAST_N:\n",
        "        for parameter in m.parameters():\n",
        "            parameter.requires_grad = True \n",
        "\n",
        "for parameter in model.transformer.ln_f.parameters():        \n",
        "    parameter.requires_grad = True\n",
        "\n",
        "for parameter in model.lm_head.parameters():        \n",
        "    parameter.requires_grad = True"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5508d70a",
      "metadata": {
        "id": "5508d70a"
      },
      "source": [
        "## Split dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "rJErBj6bTpjb",
      "metadata": {
        "id": "rJErBj6bTpjb"
      },
      "outputs": [],
      "source": [
        "data = dict()\n",
        "for id in df.index:\n",
        "    data[id] = [df[\"title\"][id], df[\"answers\"][id], df[\"yake_keyword\"][id]] # yake keywords are used because they appear to be more relevant"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "NseuptMCVNKY",
      "metadata": {
        "id": "NseuptMCVNKY"
      },
      "outputs": [],
      "source": [
        "data[0][2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "30a25f50",
      "metadata": {
        "id": "30a25f50"
      },
      "outputs": [],
      "source": [
        "train_data, val_data = split_data(data)\n",
        "\n",
        "train_dataset = myDataset(train_data, tokenizer)\n",
        "val_dataset = myDataset(val_data, tokenizer, randomize=False)\n",
        "\n",
        "f'Training sample: {len(train_dataset) :,}\\nValidation sample: {len(val_dataset) :,}'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10606c97",
      "metadata": {
        "id": "10606c97"
      },
      "source": [
        "# Finetuning GPT2 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f8047b7",
      "metadata": {
        "id": "3f8047b7"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"/content/\",\n",
        "    num_train_epochs=EPOCHS,\n",
        "    per_device_train_batch_size=TRAIN_BATCHSIZE,\n",
        "    per_device_eval_batch_size=TRAIN_BATCHSIZE,\n",
        "    gradient_accumulation_steps=BATCH_UPDATE,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    fp16=True,\n",
        "    fp16_opt_level='O1',\n",
        "    warmup_steps=WARMUP_STEPS,    \n",
        "    learning_rate=LR,\n",
        "    adam_epsilon=EPS,\n",
        "    weight_decay=0.01,        \n",
        "    save_total_limit=1,\n",
        "    load_best_model_at_end=True,     \n",
        ")\n",
        "\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,    \n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    tokenizer=tokenizer\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "HmoB_eIXhMX_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HmoB_eIXhMX_",
        "outputId": "bc4ac7b1-bb40-4010-b252-f84473a91c4e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 8000\n",
            "  Num Epochs = 4\n",
            "  Instantaneous batch size per device = 4\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 64\n",
            "  Gradient Accumulation steps = 16\n",
            "  Total optimization steps = 500\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='463' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [463/500 1:09:23 < 05:34, 0.11 it/s, Epoch 3.70/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.755551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.741121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.744387</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to /content/checkpoint-125\n",
            "Configuration saved in /content/checkpoint-125/config.json\n",
            "Model weights saved in /content/checkpoint-125/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-125/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-125/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to /content/checkpoint-250\n",
            "Configuration saved in /content/checkpoint-250/config.json\n",
            "Model weights saved in /content/checkpoint-250/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-250/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-250/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/checkpoint-125] due to args.save_total_limit\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to /content/checkpoint-375\n",
            "Configuration saved in /content/checkpoint-375/config.json\n",
            "Model weights saved in /content/checkpoint-375/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-375/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-375/special_tokens_map.json\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [500/500 1:16:26, Epoch 4/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.755551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.741121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.744387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.782400</td>\n",
              "      <td>0.750322</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to /content/checkpoint-500\n",
            "Configuration saved in /content/checkpoint-500/config.json\n",
            "Model weights saved in /content/checkpoint-500/pytorch_model.bin\n",
            "tokenizer config file saved in /content/checkpoint-500/tokenizer_config.json\n",
            "Special tokens file saved in /content/checkpoint-500/special_tokens_map.json\n",
            "Deleting older checkpoint [/content/checkpoint-375] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from /content/checkpoint-250 (score: 0.7411211133003235).\n",
            "Saving model checkpoint to /content/\n",
            "Configuration saved in /content/config.json\n",
            "Model weights saved in /content/pytorch_model.bin\n",
            "tokenizer config file saved in /content/tokenizer_config.json\n",
            "Special tokens file saved in /content/special_tokens_map.json\n"
          ]
        }
      ],
      "source": [
        "trainer.train()\n",
        "trainer.save_model()    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4jha1vGTBHHX",
      "metadata": {
        "id": "4jha1vGTBHHX"
      },
      "outputs": [],
      "source": [
        "#storing the files\n",
        "drive.mount('/content/gdrive',force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "soGF0q_zBOym",
      "metadata": {
        "id": "soGF0q_zBOym"
      },
      "outputs": [],
      "source": [
        "!cp ./pytorch_model.bin '/content/gdrive/MyDrive/model_run_2'\n",
        "!cp ./config.json '/content/gdrive/MyDrive/model_run_2'\n",
        "!cp ./tokenizer_config.json '/content/gdrive/MyDrive/model_run_2'\n",
        "!cp ./special_tokens_map.json '/content/gdrive/MyDrive/model_run_2'\n",
        "!ls -lt '/content/gdrive/MyDrive/model_run_2'"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d7d76745",
      "metadata": {
        "id": "d7d76745"
      },
      "source": [
        "# Generating Text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "32ef22b2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "32ef22b2",
        "outputId": "b378ce4a-f4dc-44d7-9ef6-1c9975a99e07"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
            "loading configuration file https://huggingface.co/gpt2/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/fc674cd6907b4c9e933cb42d67662436b89fa9540a1f40d7c919d0109289ad01.7d2e0efa5ca20cef4fb199382111e9d3ad96fd77b849e1d4bed13a66e1336f51\n",
            "Model config GPT2Config {\n",
            "  \"_name_or_path\": \"gpt2\",\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "loading file https://huggingface.co/gpt2/resolve/main/vocab.json from cache at /root/.cache/huggingface/transformers/684fe667923972fb57f6b4dcb61a3c92763ad89882f3da5da9866baf14f2d60f.c7ed1f96aac49e745788faa77ba0a26a392643a50bb388b9c04ff469e555241f\n",
            "loading file https://huggingface.co/gpt2/resolve/main/merges.txt from cache at /root/.cache/huggingface/transformers/c0c761a63004025aeadd530c4c27b860ec4ecbe8a00531233de21d865a402598.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b\n",
            "loading file https://huggingface.co/gpt2/resolve/main/tokenizer.json from cache at /root/.cache/huggingface/transformers/16a2f78023c8dc511294f0c97b5e10fde3ef9889ad6d11ffaa2a00714e73926e.cf2d0ecb83b6df91b3dbb53f1d1e4c311578bfd3aa0e04934215a49bf9898df0\n",
            "loading file https://huggingface.co/gpt2/resolve/main/added_tokens.json from cache at None\n",
            "loading file https://huggingface.co/gpt2/resolve/main/special_tokens_map.json from cache at None\n",
            "loading file https://huggingface.co/gpt2/resolve/main/tokenizer_config.json from cache at None\n",
            "loading configuration file https://huggingface.co/gpt2/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/fc674cd6907b4c9e933cb42d67662436b89fa9540a1f40d7c919d0109289ad01.7d2e0efa5ca20cef4fb199382111e9d3ad96fd77b849e1d4bed13a66e1336f51\n",
            "Model config GPT2Config {\n",
            "  \"_name_or_path\": \"gpt2\",\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "Assigning <|BOS|> to the bos_token key of the tokenizer\n",
            "Assigning <|EOS|> to the eos_token key of the tokenizer\n",
            "Assigning <|UNK|> to the unk_token key of the tokenizer\n",
            "Assigning <|PAD|> to the pad_token key of the tokenizer\n",
            "Assigning <|SEP|> to the sep_token key of the tokenizer\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Special tokens added\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "loading configuration file https://huggingface.co/gpt2/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/fc674cd6907b4c9e933cb42d67662436b89fa9540a1f40d7c919d0109289ad01.7d2e0efa5ca20cef4fb199382111e9d3ad96fd77b849e1d4bed13a66e1336f51\n",
            "Model config GPT2Config {\n",
            "  \"_name_or_path\": \"gpt2\",\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"architectures\": [\n",
            "    \"GPT2LMHeadModel\"\n",
            "  ],\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50257,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50258,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_ctx\": 1024,\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"pad_token_id\": 50260,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"sep_token_id\": 50261,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"task_specific_params\": {\n",
            "    \"text-generation\": {\n",
            "      \"do_sample\": true,\n",
            "      \"max_length\": 50\n",
            "    }\n",
            "  },\n",
            "  \"transformers_version\": \"4.15.0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n",
            "loading weights file https://huggingface.co/gpt2/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/752929ace039baa8ef70fe21cdf9ab9445773d20e733cf693d667982e210837e.323c769945a351daa25546176f8208b3004b6f563438a7603e7932bae9025925\n",
            "All model checkpoint weights were used when initializing GPT2LMHeadModel.\n",
            "\n",
            "All the weights of GPT2LMHeadModel were initialized from the model checkpoint at gpt2.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use GPT2LMHeadModel for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "tokenizer = get_tokenizer(special_tokens=SPECIAL_TOKENS)\n",
        "model = get_model(tokenizer, \n",
        "                  special_tokens=SPECIAL_TOKENS,\n",
        "                  load_model_path='pytorch_model.bin')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "6c595b88",
      "metadata": {
        "id": "6c595b88"
      },
      "outputs": [],
      "source": [
        "title = \"I have a Question.\"\n",
        "keywords = [\"business\",\"consulting\",\"fraud\"]\n",
        "kw = myDataset.join_keywords(keywords, randomize=False)\n",
        "\n",
        "prompt = SPECIAL_TOKENS['bos_token'] + title + SPECIAL_TOKENS['sep_token'] + kw + SPECIAL_TOKENS['sep_token'] \n",
        "         \n",
        "generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)\n",
        "device = torch.device(\"cuda\")\n",
        "generated = generated.to(device)\n",
        "\n",
        "model.eval();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "1625e970",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1625e970",
        "outputId": "14008ef8-9cc7-44d5-b62d-221c37dcd2ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1: What is the difference between \"concessions\" and real business?: The latter refers to any transaction you've made with someone else that involves money or property in exchange for your services (such as agreeing on how much cash/gold it will cost).  In most cases this means purchasing something at auction - which usually requires lots of other people's permission first thing before being able sell them off...but sometimes there are agreements where they're allowed some sort trade-in fee if their goods aren't sold outright so long like an agreement regarding what kind might be sent back once somebody has purchased theirs from another source....or when buying products directly without actually doing anything themselves by paying anyone up front; either way these deals tend not always take place within legitimate transactions but instead involve very complex legal arrangements involving many different kinds.: Concession comes into play here because banks typically don’t want customers signing contracts just yet anymore since bank depositors often end things abruptly after taking out loans etc., thus reducing risk around fraudulently entering one account while leaving others behind: Banks do allow withdrawals via credit cards though I'm pretty sure none were ever required prior too!\n",
            "\n",
            "\n",
            "2: Why do I need to contact an attorney if there is no one in my company?: Because it's easier for them and more convenient because they can find someone willing (or able) than not even know who you are or what kind of business the person has on your resume at all times!\n",
            "\n",
            "\n",
            "3: How do people scam you?: You're not alone in this!  We've been doing business with the banking industry for years and we know how to exploit these scams (like selling fake credit card numbers or using your name on websites).\n",
            "\n",
            "\n",
            "4: Why is it that people who work at companies and banks do not pay their employees taxes?: Because they're doing something wrong with the money (money) going to them in return for helping other businesses get away from fraud or mismanagement by stealing things off of someone else's business account.: It isn't really about whether you should be paying your workers wages but rather how much time an employer spends on payrolls like hiring managers etc..  There are many reasons why some employers might refuse certain types - particularly when there were already plenty available jobs open right around town so those weren’t as likely issues where one employee could go home without repercussions.\n",
            "\n",
            "\n",
            "5: Why does the US Government give money to companies that make fake products?: Because they do not like it and want more than their fair share of government subsidies for doing so (or at least some part)  If you are in an industry where most people don't get paid any way by businesses or other individuals then there is no incentive on your side - this means many firms will never see as much profit from fraudulent activities but instead may be seen through advertisements/promotions made about them being \"better\" because these can also help convince others who might think otherwise into signing up with us if we take action against fraudulent practices such things: I would suggest asking questions before making purchases though; what sort’t happens when someone buys something?\n",
            "\n",
            "\n",
            "6: Why do I get the impression that you are dishonest when it comes to your business?:  You're just asking for money from someone who has no real reason and is willing or able/creditable enough (or at least capable of) in order not be corruptly influenced by people with little motivation outside their personal financial interests but instead an interest they feel will help them achieve what others want through some form(s).  If there's one thing about fraudsters like me...it isn't actually doing anything good - if something does go wrong because somebody else messed up how things work without permission..and then gets caught out as well....but honestly.....you'd think this would never happen unless all these scams were stopped so everyone could pay off debts more quickly than before!\n",
            "\n",
            "\n",
            "7: What is fraud? How do companies like Apple and Microsoft (and other large tech firms) scam customers?:  The answer to your question depends on the type of company you are referring about: They usually use an intermediary who provides information or services that they think will benefit their business interests rather than something else entirely - as in if somebody from Google just sent them $100 million for some sort marketing campaign! A typical example would be advertising with \"hey I need ads\" which basically says 'you're interested because this ad helps me make more money'. This kind isn't necessarily illegal but it's still misleading so don’t get too worked up by asking why someone has gone through all these trouble when he was not actually trying anything at any point before him...\n",
            "\n",
            "\n",
            "8: Why do I need to contact the person that you are referring about fraud?: Because there's always some shady guy on their payroll who may be able or willing and can help them out in an effort of making things more difficult for themselves by selling off stuff they don't really own (like credit cards).  The real problem is if your client isn’t legit it becomes very hard “to trust him/her because he doesn´re just another scammer trying too much at his job with no one stopping any attempts made!\n",
            "\n",
            "\n",
            "9: How can I be accused of fraud if the business fails to pay my taxes?: You are not legally required by law that you willfully misrepresent your credit card information in order for it (as defined below).  If there is no charge made or accepted on account with an employer's payroll system under this paragraph and they fail completely within three years after their receipt from one company--and then later make more payments due than expected based upon such failure --they may pursue criminal charges against them as evidence beyond reasonable doubt provided proof was obtained through proper investigation without undue delay; however these actions must meet certain requirements: 1) The fraudulent act has been committed at least five times since January 2 2015 but less often when employees were terminated because somebody failed during any part(s), 3-4 months before termination deadline unless all relevant facts had already emerged about misconduct which would justify his/her removal - 5%+1=20%. This means only those who knowingly misused money received while employed could face prosecution...but also some people whose employment expired prior year might still qualify despite being removed immediately following discovery....or even just recently fired entirely once found guilty over another matter where he didn't actually do anything wrong!\n",
            "\n",
            "\n",
            "10: What is the purpose of fraudulently contacting someone in my business? How do they get to know you and what information does your company need?: Fraudulent contact with an agent or broker will usually be done through emails (for example).  The person who makes that email might also want some sort - like asking for permission from their boss at work as well if there's no other way out but he knows exactly where his client needs them/his services etc...\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "sample_outputs = model.generate(generated, \n",
        "                                do_sample=True,   \n",
        "                                min_length=50, \n",
        "                                max_length=MAXLEN,\n",
        "                                top_k=30,                                 \n",
        "                                top_p=0.7,        \n",
        "                                temperature=0.9,\n",
        "                                repetition_penalty=2.0,\n",
        "                                num_return_sequences=10\n",
        "                                )\n",
        "\n",
        "for i, sample_output in enumerate(sample_outputs):\n",
        "    text = tokenizer.decode(sample_output, skip_special_tokens=True)\n",
        "    a = len(title) + len(','.join(keywords))    \n",
        "    print(\"{}: {}\\n\\n\".format(i+1,  text[a:]))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "FMmVEVzOiT55",
      "metadata": {
        "id": "FMmVEVzOiT55"
      },
      "source": [
        "# How to use\n",
        "\n",
        "The model is uploaded to https://huggingface.co/Madhour/gpt2-eli5. Follow the instructions there to use this pre-trained model."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "nzPOFsHciniE",
      "metadata": {
        "id": "nzPOFsHciniE"
      },
      "source": [
        "# References\n",
        "Parts of the code were drawn and/or inspired from Ivan Lai's article on [conditional text generation](https://towardsdatascience.com/conditional-text-generation-by-fine-tuning-gpt-2-11c1a9fc639d). In his [colab notebook](https://colab.research.google.com/drive/1vnpMoZoenRrWeaxMyfYK4DDbtlBu-M8V?usp=sharing#scrollTo=ZwRhz144Fknp), he explained in full detail how to finetune a pre-trained huggingface transformer model.\n",
        "\n",
        "The eli5 dataset is appropriated from [Fan et al.](https://doi.org/10.18653/v1/p19-1346) and was downloaded using the huggingface dataset library."
      ]
    }
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