Fine_Grained_Financial_Sentiment_Regression_with_BERT.ipynb

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    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sfuller14/MSDS-453-Project/blob/master/Fine_Grained_Financial_Sentiment_Regression_with_BERT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "4ULfd994FzGq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import output"
      ],
      "metadata": {
        "id": "u5NmOphTlO12"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Comment this out for deberta\n",
        "!pip install -q transformers"
      ],
      "metadata": {
        "id": "s4D97mq9uCOW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets"
      ],
      "metadata": {
        "id": "b1d0XltxHsXD"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "import numpy as np\n",
        "import json\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "\n",
        "from transformers import AutoTokenizer, DataCollatorWithPadding, TFAutoModelForSequenceClassification\n",
        "from datasets import Dataset\n",
        "\n",
        "from pprint import pprint"
      ],
      "metadata": {
        "id": "_DnXH96IOciA"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.model_selection import KFold\n",
        "from scipy.spatial.distance import cosine\n",
        "\n",
        "# For model evaluation:\n",
        "def cosine_score(predicted_values, true_values):\n",
        "    return 1 - cosine(predicted_values, true_values)"
      ],
      "metadata": {
        "id": "ejRgsYsbK9MD"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "pFtbA6TV85yG"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "WDm0LKd_E_z8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get Data"
      ],
      "metadata": {
        "id": "5itXn0z4FOfi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get data from here:\n",
        "# !git clone https://bitbucket.org/ssix-project/semeval-2017-task-5-subtask-2.git"
      ],
      "metadata": {
        "id": "s_2atzBm47jl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean up ground truth test set\n",
        "import json\n",
        "\n",
        "with open(\"data/finance/Headlines_Testdata_withscores.json\", \"r\") as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "for example in data:\n",
        "    del example['UniqueID']\n",
        "\n",
        "with open(\"data/finance/Headlines_Testdata_withscores_clean.json\", \"w\") as file:\n",
        "    json.dump(data, file)"
      ],
      "metadata": {
        "id": "brsfUp8UCwNr"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_train_ds = Dataset.from_json(\"data/finance/Headline_Trainingdata.json\")\n",
        "raw_val_ds = Dataset.from_json(\"data/finance/Headline_Validationdata.json\")\n",
        "raw_test_ds = Dataset.from_json(\"data/finance/Headlines_Testdata_withscores_clean.json\")\n",
        "raw_datasets = {\"train\": raw_train_ds, \"val\": raw_val_ds, \"test\": raw_test_ds}\n",
        "output.clear()"
      ],
      "metadata": {
        "id": "oNs-hTsIFQsL"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_train_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pcgm56fWDYUd",
        "outputId": "80b020fb-ae39-4dcf-e1f7-7e9d450f5345"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'company', 'title', 'sentiment'],\n",
              "    num_rows: 914\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_val_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSt3iVYWoPS7",
        "outputId": "bd309bb9-7ba3-45b2-a555-cedd21c8a5df"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'company', 'title', 'sentiment'],\n",
              "    num_rows: 228\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_test_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGmAu1lRA9Oj",
        "outputId": "fc8eb47a-eff5-416c-d69f-cd8e800666c0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'company', 'title', 'sentiment'],\n",
              "    num_rows: 491\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examine data"
      ],
      "metadata": {
        "id": "SDyt9KF2HgY5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(raw_train_ds.to_dict())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "mX5LY6tl8NWi",
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              "       id             company  \\\n",
              "0       3                 IMI   \n",
              "1       4            Glencore   \n",
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              "..    ...                 ...   \n",
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              "\n",
              "                                                                                                      title  \\\n",
              "0                                       IMI posts drop in first-quarter organic revenue; warns on full year   \n",
              "1                                              Glencore to refinance its short-term debt early, shares rise   \n",
              "2                                           EasyJet attracts more passengers in June but still lags Ryanair   \n",
              "3                                                                    Barclays 'bad bank' chief to step down   \n",
              "4                                            Bilfinger Industrial Services win Â£100m BP contract extension   \n",
              "..                                                                                                      ...   \n",
              "909  Balfour Beatty plc Set To Reinstate Dividend (And Rival National Grid plc And Centrica PLC Once More?)   \n",
              "910                                    UPDATE: CIB, Legal & General Sell Egyptian Life Joint Venture To AXA   \n",
              "911                                                               BG Group appoints new CEO one month early   \n",
              "912                                           Barclays set to name former JPMorgan banker Staley as new CEO   \n",
              "913                                         Kingfisher share price slides on cost to implement new strategy   \n",
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              "                                                     [key], {});\n",
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        "pd.DataFrame(raw_val_ds.to_dict())"
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        "id": "VjrHYNH6oY-1",
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      "execution_count": 39,
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            "text/plain": [
              "      id           company  \\\n",
              "0    155          Starwood   \n",
              "1     71   Dixons Carphone   \n",
              "2    336      Severn Trent   \n",
              "3     58                BP   \n",
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              "..   ...               ...   \n",
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              "226  210       AstraZeneca   \n",
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              "                                                                title  \\\n",
              "0        Intercontinental Hotels, Starwood held early deal talks - FT   \n",
              "1                      CompaniesLord Livingston joins Dixons Carphone   \n",
              "2    Water utility Severn Trent ups savings forecast, FY profit falls   \n",
              "3         EXCLUSIVE-BP, China's CNPC to unveil oil alliance - sources   \n",
              "4      BAE Systems's sales boosted by European Typhoon and currencies   \n",
              "..                                                                ...   \n",
              "223                United Utilities FY profit up 3.5%; to boost capex   \n",
              "224       Shell and BG Shareholders to Vote on Deal at End of January   \n",
              "225       BP sees Q1 earnings slide as low oil prices take their toll   \n",
              "226   AstraZeneca in Talks to Buy Cancer Drug Developer Acerta Pharma   \n",
              "227         Tesco bans sugary drinks in â€œchildhood obesity driveâ€퀀   \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(raw_test_ds.to_dict())"
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        "id": "RZXzVIgG8p8T",
        "outputId": "2f636732-4157-4b0e-e054-7401e9cd38d2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id          company  \\\n",
              "0    1144          Ashtead   \n",
              "1    1145            Shell   \n",
              "2    1146       Prudential   \n",
              "3    1147  GlaxoSmithKline   \n",
              "4    1148         Barclays   \n",
              "..    ...              ...   \n",
              "486  1630         Barclays   \n",
              "487  1631         Barclays   \n",
              "488  1632         Glencore   \n",
              "489  1633              GSK   \n",
              "490  1634      AstraZeneca   \n",
              "\n",
              "                                                                                        title  \\\n",
              "0                                Ashtead to buy back shares, full-year profit beats estimates   \n",
              "1                                            EU regulators clear Shell's takeover of BG Group   \n",
              "2                            UK's FTSE has worst day so far in 2015 as BG and Prudential fall   \n",
              "3                                                         GlaxoSmithKline acquires HIV assets   \n",
              "4                                                     Barclays faces another heavy forex fine   \n",
              "..                                                                                        ...   \n",
              "486  Exclusive: Barclays reins in foreign exchange trading before referendum result - sources   \n",
              "487                             Copper Falls With Mining Stocks as Barclays Warns of Pullback   \n",
              "488                                       LPC-Glencore launches refinancing of US$8.45bn loan   \n",
              "489                                GSK and Novartis complete deals to reshape both drugmakers   \n",
              "490                                 Sanofi poaches AstraZeneca scientist as new research head   \n",
              "\n",
              "     sentiment  \n",
              "0        0.588  \n",
              "1        0.276  \n",
              "2       -0.651  \n",
              "3        0.390  \n",
              "4       -0.834  \n",
              "..         ...  \n",
              "486      0.152  \n",
              "487     -0.800  \n",
              "488      0.173  \n",
              "489      0.285  \n",
              "490     -0.058  \n",
              "\n",
              "[491 rows x 4 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>company</th>\n",
              "      <th>title</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1144</td>\n",
              "      <td>Ashtead</td>\n",
              "      <td>Ashtead to buy back shares, full-year profit beats estimates</td>\n",
              "      <td>0.588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1145</td>\n",
              "      <td>Shell</td>\n",
              "      <td>EU regulators clear Shell's takeover of BG Group</td>\n",
              "      <td>0.276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1146</td>\n",
              "      <td>Prudential</td>\n",
              "      <td>UK's FTSE has worst day so far in 2015 as BG and Prudential fall</td>\n",
              "      <td>-0.651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1147</td>\n",
              "      <td>GlaxoSmithKline</td>\n",
              "      <td>GlaxoSmithKline acquires HIV assets</td>\n",
              "      <td>0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1148</td>\n",
              "      <td>Barclays</td>\n",
              "      <td>Barclays faces another heavy forex fine</td>\n",
              "      <td>-0.834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486</th>\n",
              "      <td>1630</td>\n",
              "      <td>Barclays</td>\n",
              "      <td>Exclusive: Barclays reins in foreign exchange trading before referendum result - sources</td>\n",
              "      <td>0.152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487</th>\n",
              "      <td>1631</td>\n",
              "      <td>Barclays</td>\n",
              "      <td>Copper Falls With Mining Stocks as Barclays Warns of Pullback</td>\n",
              "      <td>-0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>1632</td>\n",
              "      <td>Glencore</td>\n",
              "      <td>LPC-Glencore launches refinancing of US$8.45bn loan</td>\n",
              "      <td>0.173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>1633</td>\n",
              "      <td>GSK</td>\n",
              "      <td>GSK and Novartis complete deals to reshape both drugmakers</td>\n",
              "      <td>0.285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490</th>\n",
              "      <td>1634</td>\n",
              "      <td>AstraZeneca</td>\n",
              "      <td>Sanofi poaches AstraZeneca scientist as new research head</td>\n",
              "      <td>-0.058</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>491 rows × 4 columns</p>\n",
              "</div>\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenize datasets"
      ],
      "metadata": {
        "id": "rTHr7Gd-KH_X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Get Max Tokenized Vector Length"
      ],
      "metadata": {
        "id": "fsfsf0XLTI9Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_max_tokenized_length(ds, tok):\n",
        "    max_len_raw = 0\n",
        "    max_len_token_vector = 0\n",
        "    \n",
        "    for text_input in ds:\n",
        "        \n",
        "        if len(text_input.split(\" \")) > max_len_raw:\n",
        "            max_len_raw = len(text_input.split(\" \"))\n",
        "        \n",
        "        if len(tok(text_input)['input_ids']) > max_len_token_vector:\n",
        "            max_len_token_vector = len(tok(text_input)['input_ids'])\n",
        "\n",
        "    return max_len_raw, max_len_token_vector"
      ],
      "metadata": {
        "id": "2DJNcxJ_R1_P"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "max_len_raw, max_len_token_vector = get_max_tokenized_length(raw_train_ds['title'] + raw_val_ds['title'] + raw_test_ds['title'], tok=tokenizer)\n",
        "output.clear()"
      ],
      "metadata": {
        "id": "Bjtfcen-vN3m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('For AutoTokenzier associated with \"bert-base-uncased\":')\n",
        "print('(Note this will potentially be different for each pretrained model)\\n')\n",
        "print(f'max_len_raw (longest headline)= {max_len_raw}')\n",
        "print(f'max_len_tokenized (longest tokenized headline)= {max_len_token_vector}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLXILrGjFhKu",
        "outputId": "7b1778f6-c6aa-47b5-de01-5977b179d865"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For AutoTokenzier associated with \"bert-base-uncased\":\n",
            "(Note this will potentially be different for each pretrained model)\n",
            "\n",
            "max_len_raw (longest headline)= 18\n",
            "max_len_tokenized (longest tokenized headline)= 29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tokenize and save as TF Dataset object"
      ],
      "metadata": {
        "id": "mQVhCif0IZsp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_tokenized_tf_datasets(untokenized_datasets_dict, tok, max_len):\n",
        "\n",
        "    def preprocess_function(examples):#, max_len, tok):\n",
        "        label = examples[\"sentiment\"] \n",
        "        examples = tok(examples[\"title\"], truncation=True, padding=\"max_length\", max_length=max_len)\n",
        "        \n",
        "        # Change this to real number\n",
        "        examples[\"label\"] = float(label)\n",
        "        return examples\n",
        "\n",
        "\n",
        "    tokenized_datasets = dict()\n",
        "    for split in untokenized_datasets_dict:\n",
        "        tokenized_datasets[split] = untokenized_datasets_dict[split].map(preprocess_function)\n",
        "\n",
        "    data_collator = DataCollatorWithPadding(tokenizer=tok, return_tensors=\"tf\")\n",
        "\n",
        "    tf_train_dataset = tokenized_datasets[\"train\"].to_tf_dataset(\n",
        "        columns=[\"attention_mask\", \"input_ids\"],#, \"token_type_ids\"],\n",
        "        label_cols=[\"labels\"],\n",
        "        shuffle=True,\n",
        "        collate_fn=data_collator,\n",
        "        batch_size=4,\n",
        "    )\n",
        "\n",
        "    tf_val_dataset = tokenized_datasets[\"val\"].to_tf_dataset(\n",
        "        columns=[\"attention_mask\", \"input_ids\"],#, \"token_type_ids\"],\n",
        "        label_cols=[\"labels\"],\n",
        "        shuffle=True,\n",
        "        collate_fn=data_collator,\n",
        "        batch_size=4,\n",
        "    )    \n",
        "\n",
        "    tf_test_dataset = tokenized_datasets[\"test\"].to_tf_dataset(\n",
        "        columns=[\"attention_mask\", \"input_ids\"],#, \"token_type_ids\"],\n",
        "        label_cols=[\"labels\"],\n",
        "        shuffle=False,\n",
        "        collate_fn=data_collator,\n",
        "        batch_size=4,\n",
        "    )\n",
        "\n",
        "    return tokenized_datasets, tf_train_dataset, tf_val_dataset, tf_test_dataset"
      ],
      "metadata": {
        "id": "hOLEXAilMD2N"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets, tf_train_dataset, tf_val_dataset, tf_test_dataset = create_tokenized_tf_datasets(untokenized_datasets_dict=raw_datasets, tok=tokenizer, max_len=max_len_token_vector)"
      ],
      "metadata": {
        "id": "tMDhLUPQvUn_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Look at tokenized dataset"
      ],
      "metadata": {
        "id": "Fs2Gpsx_Ila0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Full training set\n",
        "np.asarray(tokenized_datasets['train']['input_ids'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-MLGVKZXoLn",
        "outputId": "262bf718-17f5-410e-c97c-326d6c435058"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  101, 10047,  2072, ...,     0,     0,     0],\n",
              "       [  101,  8904, 17345, ...,     0,     0,     0],\n",
              "       [  101,  3733, 15759, ...,     0,     0,     0],\n",
              "       ...,\n",
              "       [  101,  1038,  2290, ...,     0,     0,     0],\n",
              "       [  101, 23724,  2015, ...,     0,     0,     0],\n",
              "       [  101,  2332,  7529, ...,     0,     0,     0]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First sentence observation -- tokenized representation\n",
        "np.asarray(tokenized_datasets['train']['input_ids'])[0,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llbG6CDvYju1",
        "outputId": "eafeeeea-9de3-4251-c272-62e10df3594a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  101, 10047,  2072,  8466,  4530,  1999,  2034,  1011,  4284,\n",
              "        7554,  6599,  1025, 19428,  2006,  2440,  2095,   102,     0,\n",
              "           0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "           0,     0])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# (training_set_rows, max_token_vector_length)\n",
        "np.asarray(tokenized_datasets['train']['input_ids']).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaMIvt1SYSnH",
        "outputId": "fdd798f0-f112-4979-8bdf-cc581c587d8b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(914, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert back from tokenized to text representation\n",
        "tokenizer.decode(tokenizer(raw_train_ds['title'][6])['input_ids'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "f7b897b0-23fb-4f08-cb46-e9a27a1d91ff",
        "id": "hBf8i5QUPOOz"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[CLS] centrica prepared for takeover approach - chairman [SEP]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Same, represented as tokens\n",
        "# 101 - [CLS]\n",
        "# 102 - [SEP]\n",
        "tokenizer(raw_train_ds['title'][6])['input_ids']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Kgwe77aPTlH",
        "outputId": "779b7b72-ba31-4955-de01-a3cb029b755f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[101, 9358, 14735, 4810, 2005, 15336, 3921, 1011, 3472, 102]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Batch #1:\n",
        "\n",
        "\n",
        "# [tokenized sentences,\n",
        "#  N/A (not used by BERT),\n",
        "#  attention masks,\n",
        "#  labels]\n",
        "\n",
        "arr = tf_train_dataset.as_numpy_iterator()\n",
        "l = list(arr)\n",
        "l[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swXs4NVFMwoL",
        "outputId": "1a8561e6-2b6d-42c3-9849-eb7ac581453a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "({'input_ids': array([[  101,  2866, 15187,  2062,  1997,  6746,  2015,  2924,  8406,\n",
              "           1010,  2260,  1012,  1019,  4551,  7038,  2992,  2061,  2521,\n",
              "            102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0],\n",
              "         [  101, 10975, 12672, 19909,  6573,  2044,  9387, 10632,  1037,\n",
              "          29646,  2509,  1012,  1023, 24700,  2013,  1049,  1004,  1043,\n",
              "            102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0],\n",
              "         [  101, 10651,  1019,  1011, 23724,  2015,  3472, 11338, 23511,\n",
              "          19589,  5766,  2000,  3177,  2039,  6143,  2689,   102,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0],\n",
              "         [  101,  2866,  2231,  7659,  8406,  1999,  6746,  2015,  2000,\n",
              "           2917,  2340,  3867,   102,     0,     0,     0,     0,     0,\n",
              "              0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "              0,     0]]),\n",
              "  'token_type_ids': array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0],\n",
              "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0],\n",
              "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0],\n",
              "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0]]),\n",
              "  'attention_mask': array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0],\n",
              "         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0],\n",
              "         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0],\n",
              "         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0]])},\n",
              " array([-0.263, -0.778, -0.229, -0.18 ], dtype=float32))"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create function to fit model"
      ],
      "metadata": {
        "id": "0QXC3XWLjKFP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_compile_fit_eval_model(checkpoint, train_ds, val_ds, test_df):\n",
        "    model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=1, ignore_mismatched_sizes=True, from_pt=True) # num_labels=1 --> regression head after BERT layer (linear layer for output)\n",
        "    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5), loss=\"mse\", metrics=['cosine_proximity'])\n",
        "    early_stopping_monitor = EarlyStopping(patience=6)\n",
        "    \n",
        "    model.fit(train_ds, epochs=50, validation_data=val_ds, callbacks=[early_stopping_monitor])# batch_size=4,\n",
        "\n",
        "    predicted_sentiments = model.predict(test_df)\n",
        "    result = cosine_score(predicted_sentiments['logits'][:,0],  np.concatenate([y for x, y in test_df], axis=0))\n",
        "    return result"
      ],
      "metadata": {
        "id": "XmPRkUJhjQJP"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tenfold_cross_val(train_ds, checkpoint):\n",
        "\n",
        "    all_results = []\n",
        "\n",
        "    kfold = KFold(n_splits=10, shuffle=True, random_state=42)\n",
        "    for i, (train_index, test_index) in enumerate(kfold.split(train_ds['title'], train_ds['sentiment'])):\n",
        "        \n",
        "        print(f\"Fold {i}:\")\n",
        "        print(f\"  Train: index={train_index}\")\n",
        "        print(f\"  Test:  index={test_index}\")\n",
        "\n",
        "        tokenized_cv_train = Dataset.from_dict(train_ds[train_index]).map(preprocess_function, remove_columns=['id', 'company', 'title', 'sentiment'])\n",
        "        tokenized_cv_test = Dataset.from_dict(train_ds[test_index]).map(preprocess_function, remove_columns=['id', 'company', 'title', 'sentiment'])\n",
        "\n",
        "        data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")\n",
        "\n",
        "        cv_train_dataset_tf = tokenized_cv_train.to_tf_dataset(\n",
        "            columns=[\"attention_mask\", \"input_ids\"],# \"token_type_ids\"],\n",
        "            label_cols=[\"labels\"],\n",
        "            shuffle=False,\n",
        "            collate_fn=data_collator,\n",
        "            batch_size=4,\n",
        "        )\n",
        "\n",
        "        cv_test_dataset_tf = tokenized_cv_test.to_tf_dataset(\n",
        "            columns=[\"attention_mask\", \"input_ids\"],# \"token_type_ids\"],\n",
        "            label_cols=[\"labels\"],\n",
        "            shuffle=False,\n",
        "            collate_fn=data_collator,\n",
        "            batch_size=4,\n",
        "        )\n",
        "    \n",
        "        model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=1, ignore_mismatched_sizes=True, from_pt=True) # num_labels=1 --> regression head after BERT layer (linear layer for output)\n",
        "        model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5), loss=\"mse\", metrics=['cosine_proximity'])\n",
        "        model.fit(cv_train_dataset_tf, epochs=10)\n",
        "\n",
        "        predicted_sentiments = model.predict(cv_test_dataset_tf)\n",
        "        result = cosine_score(predicted_sentiments['logits'][:,0],  np.concatenate([y for x, y in cv_test_dataset_tf], axis=0))\n",
        "        print(f'Fold {i} cosine_similarity = {result}\\n\\n')\n",
        "        all_results.append(result)\n",
        "\n",
        "    return all_results"
      ],
      "metadata": {
        "id": "uhrbJNWLCiVa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BERT\n",
        "bert-base-uncased  \n",
        "Cross Validation cosine_similarity = 74.94  \n",
        "Competition Test Set cosine_similarity = 79.26"
      ],
      "metadata": {
        "id": "oP5sMvNiFsNK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing for input into BERT"
      ],
      "metadata": {
        "id": "VYpK1RvtMUvL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "current = \"bert-base-uncased\""
      ],
      "metadata": {
        "id": "zdI6meSgTBXG"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(current)\n",
        "max_len_raw, max_len_token_vector = get_max_tokenized_length(raw_train_ds['title'] + raw_test_ds['title'], tok=tokenizer)\n",
        "\n",
        "print(f'For AutoTokenzier associated with \"{current}\":')\n",
        "print('(Note this will potentially be different for each pretrained model)\\n')\n",
        "print(f'max_len_raw (longest headline)= {max_len_raw}')\n",
        "print(f'max_len_tokenized (longest tokenized headline)= {max_len_token_vector}')"
      ],
      "metadata": {
        "id": "i6rVLe3JL3BF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12aec662-302a-4938-83dd-6b45f704b1e7"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For AutoTokenzier associated with \"bert-base-uncased\":\n",
            "(Note this will potentially be different for each pretrained model)\n",
            "\n",
            "max_len_raw (longest headline)= 18\n",
            "max_len_tokenized (longest tokenized headline)= 29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets, tf_train_dataset, tf_val_dataset, tf_test_dataset = create_tokenized_tf_datasets(untokenized_datasets_dict = raw_datasets, \n",
        "                                                                                                     tok = tokenizer, \n",
        "                                                                                                     max_len = max_len_token_vector)\n",
        "output.clear()"
      ],
      "metadata": {
        "id": "hkAs5Q8mHKON"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit and Evaluate BERT"
      ],
      "metadata": {
        "id": "GOh_TBGOzV3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "load_compile_fit_eval_model(current, tf_train_dataset, tf_val_dataset, tf_test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dAdUgKdtkcN2",
        "outputId": "fcb6c8d4-60e0-408b-9cf3-91a82b66eaca"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "229/229 [==============================] - 30s 68ms/step - loss: 0.1296 - cosine_proximity: 0.3873 - val_loss: 0.0657 - val_cosine_proximity: 0.7281\n",
            "Epoch 2/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0657 - cosine_proximity: 0.6937 - val_loss: 0.0557 - val_cosine_proximity: 0.7193\n",
            "Epoch 3/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0404 - cosine_proximity: 0.7746 - val_loss: 0.0536 - val_cosine_proximity: 0.7632\n",
            "Epoch 4/50\n",
            "229/229 [==============================] - 13s 57ms/step - loss: 0.0293 - cosine_proximity: 0.8140 - val_loss: 0.0498 - val_cosine_proximity: 0.7632\n",
            "Epoch 5/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0221 - cosine_proximity: 0.8337 - val_loss: 0.0578 - val_cosine_proximity: 0.7018\n",
            "Epoch 6/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0191 - cosine_proximity: 0.8425 - val_loss: 0.0524 - val_cosine_proximity: 0.7105\n",
            "Epoch 7/50\n",
            "229/229 [==============================] - 13s 58ms/step - loss: 0.0195 - cosine_proximity: 0.8687 - val_loss: 0.0494 - val_cosine_proximity: 0.7368\n",
            "Epoch 8/50\n",
            "229/229 [==============================] - 13s 57ms/step - loss: 0.0143 - cosine_proximity: 0.8490 - val_loss: 0.0543 - val_cosine_proximity: 0.7368\n",
            "Epoch 9/50\n",
            "229/229 [==============================] - 13s 57ms/step - loss: 0.0156 - cosine_proximity: 0.8753 - val_loss: 0.0492 - val_cosine_proximity: 0.7456\n",
            "Epoch 10/50\n",
            "229/229 [==============================] - 13s 57ms/step - loss: 0.0146 - cosine_proximity: 0.8687 - val_loss: 0.0499 - val_cosine_proximity: 0.7456\n",
            "Epoch 11/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0135 - cosine_proximity: 0.8731 - val_loss: 0.0517 - val_cosine_proximity: 0.7193\n",
            "Epoch 12/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0137 - cosine_proximity: 0.8862 - val_loss: 0.0475 - val_cosine_proximity: 0.7368\n",
            "Epoch 13/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0148 - cosine_proximity: 0.8687 - val_loss: 0.0477 - val_cosine_proximity: 0.7544\n",
            "Epoch 14/50\n",
            "229/229 [==============================] - 13s 57ms/step - loss: 0.0133 - cosine_proximity: 0.8687 - val_loss: 0.0479 - val_cosine_proximity: 0.7018\n",
            "Epoch 15/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0138 - cosine_proximity: 0.8753 - val_loss: 0.0526 - val_cosine_proximity: 0.7456\n",
            "Epoch 16/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0126 - cosine_proximity: 0.8643 - val_loss: 0.0466 - val_cosine_proximity: 0.7456\n",
            "Epoch 17/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0119 - cosine_proximity: 0.8906 - val_loss: 0.0536 - val_cosine_proximity: 0.7018\n",
            "Epoch 18/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0128 - cosine_proximity: 0.8840 - val_loss: 0.0531 - val_cosine_proximity: 0.7018\n",
            "Epoch 19/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0118 - cosine_proximity: 0.8818 - val_loss: 0.0540 - val_cosine_proximity: 0.7632\n",
            "Epoch 20/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0109 - cosine_proximity: 0.8796 - val_loss: 0.0493 - val_cosine_proximity: 0.7193\n",
            "Epoch 21/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0113 - cosine_proximity: 0.8928 - val_loss: 0.0539 - val_cosine_proximity: 0.7018\n",
            "Epoch 22/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0119 - cosine_proximity: 0.8862 - val_loss: 0.0505 - val_cosine_proximity: 0.7193\n",
            "123/123 [==============================] - 6s 24ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7926343679428101"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perform 10-Fold Cross Validation\n",
        "(as a proxy for test set - prior to receiving ground truth test set)"
      ],
      "metadata": {
        "id": "YvS7Wb7cNwjG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv_raw_data_cosine_similarity_all_folds = tenfold_cross_val(raw_train_ds)\n",
        "cv_raw_data_cosine_similarity_avg = (sum(cv_raw_data_cosine_similarity_all_folds) / len(cv_raw_data_cosine_similarity_all_folds)) * 100"
      ],
      "metadata": {
        "id": "sEUTcox08Xy3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_raw_data_cosine_similarity_avg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqm707vlY4Mt",
        "outputId": "db9ad506-f4c2-41ca-d651-675bc15d2740"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "74.94607746601105"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# finBERT\n",
        "ProsusAI/finbert  \n",
        "Cross Validation cosine_similarity = 77.79  \n",
        "Competition Test Set cosine_similarity = 80.69"
      ],
      "metadata": {
        "id": "NBJLkk9FaW9x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing for input into finBERT"
      ],
      "metadata": {
        "id": "rUCjeGOgbn5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "current = \"ProsusAI/finbert\""
      ],
      "metadata": {
        "id": "2PicX7DqrUDe"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(current)\n",
        "max_len_raw, max_len_token_vector = get_max_tokenized_length(raw_train_ds['title'] + raw_test_ds['title'], tok=tokenizer)\n",
        "\n",
        "print(f'For AutoTokenzier associated with \"{current}\":')\n",
        "print('(Note this will potentially be different for each pretrained model)\\n')\n",
        "print(f'max_len_raw (longest headline)= {max_len_raw}')\n",
        "print(f'max_len_tokenized (longest tokenized headline)= {max_len_token_vector}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236,
          "referenced_widgets": [
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            "09e8936ded1c4223b7d65711e2f5061f",
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        },
        "outputId": "4f623740-13d0-4a52-d4d1-624bbe780754",
        "id": "EwYB-yEorUDf"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/252 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ec610a7a3f684eaf9e1270e66b8af2d0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For AutoTokenzier associated with \"ProsusAI/finbert\":\n",
            "(Note this will potentially be different for each pretrained model)\n",
            "\n",
            "max_len_raw (longest headline)= 18\n",
            "max_len_tokenized (longest tokenized headline)= 29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets, tf_train_dataset, tf_val_dataset, tf_test_dataset = create_tokenized_tf_datasets(untokenized_datasets_dict = raw_datasets, \n",
        "                                                                                                     tok = tokenizer, \n",
        "                                                                                                     max_len = max_len_token_vector)\n",
        "output.clear()"
      ],
      "metadata": {
        "id": "JWZx4oTkrUDf"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit and Evaluate finBERT"
      ],
      "metadata": {
        "id": "XTSo-TKCzbpj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "load_compile_fit_eval_model(current, tf_train_dataset, tf_val_dataset, tf_test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739,
          "referenced_widgets": [
            "6b4c68df6e3644b981111dc925564fd2",
            "6d2b334a082445bf93d728747a410965",
            "a117e0c9226949c8b897d83ab22638ec",
            "593ea88f231340cc99427e6489e89f12",
            "df1cb7911aac4e668d5f98a1d14c6d98",
            "bb142438a6014abebaff49947e236a2a",
            "13f10ccc0d0040d485a4677388119125",
            "d3e6ef39db0b4f4e84bd2e3eb2d126c3",
            "e430eef6e55e4ceb9614bf2b3415c4eb",
            "a3194232713d4ea6a4a47f2b89d77964",
            "3e90d32f7cb34fc99a65d6dc138f0730"
          ]
        },
        "outputId": "cb258f8d-f6f5-4ec4-fbc2-5402093d0e8d",
        "id": "624DDr7BrUDg"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"tf_model.h5\";:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b4c68df6e3644b981111dc925564fd2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at ProsusAI/finbert and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "229/229 [==============================] - 31s 68ms/step - loss: 0.1142 - cosine_proximity: 0.4398 - val_loss: 0.0574 - val_cosine_proximity: 0.6842\n",
            "Epoch 2/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0586 - cosine_proximity: 0.6499 - val_loss: 0.0587 - val_cosine_proximity: 0.7105\n",
            "Epoch 3/50\n",
            "229/229 [==============================] - 13s 58ms/step - loss: 0.0447 - cosine_proximity: 0.7243 - val_loss: 0.0499 - val_cosine_proximity: 0.7018\n",
            "Epoch 4/50\n",
            "229/229 [==============================] - 13s 57ms/step - loss: 0.0362 - cosine_proximity: 0.7637 - val_loss: 0.0520 - val_cosine_proximity: 0.7018\n",
            "Epoch 5/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0303 - cosine_proximity: 0.7921 - val_loss: 0.0568 - val_cosine_proximity: 0.6491\n",
            "Epoch 6/50\n",
            "229/229 [==============================] - 13s 57ms/step - loss: 0.0251 - cosine_proximity: 0.8053 - val_loss: 0.0570 - val_cosine_proximity: 0.7456\n",
            "Epoch 7/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0226 - cosine_proximity: 0.8249 - val_loss: 0.0519 - val_cosine_proximity: 0.7018\n",
            "Epoch 8/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0190 - cosine_proximity: 0.8337 - val_loss: 0.0483 - val_cosine_proximity: 0.7281\n",
            "Epoch 9/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0165 - cosine_proximity: 0.8600 - val_loss: 0.0517 - val_cosine_proximity: 0.7105\n",
            "Epoch 10/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0163 - cosine_proximity: 0.8600 - val_loss: 0.0471 - val_cosine_proximity: 0.7544\n",
            "Epoch 11/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0154 - cosine_proximity: 0.8556 - val_loss: 0.0516 - val_cosine_proximity: 0.7193\n",
            "Epoch 12/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0135 - cosine_proximity: 0.8643 - val_loss: 0.0486 - val_cosine_proximity: 0.7368\n",
            "Epoch 13/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0119 - cosine_proximity: 0.8862 - val_loss: 0.0484 - val_cosine_proximity: 0.7193\n",
            "Epoch 14/50\n",
            "229/229 [==============================] - 13s 57ms/step - loss: 0.0122 - cosine_proximity: 0.8775 - val_loss: 0.0540 - val_cosine_proximity: 0.6842\n",
            "Epoch 15/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0113 - cosine_proximity: 0.8972 - val_loss: 0.0500 - val_cosine_proximity: 0.7368\n",
            "Epoch 16/50\n",
            "229/229 [==============================] - 13s 56ms/step - loss: 0.0117 - cosine_proximity: 0.8818 - val_loss: 0.0517 - val_cosine_proximity: 0.7105\n",
            "123/123 [==============================] - 6s 24ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8069681525230408"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perform 10-Fold Cross Validation\n",
        "(as a proxy for test set - prior to receiving ground truth test set)"
      ],
      "metadata": {
        "id": "pSKe-qEIbn5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv_raw_data_cosine_similarity_all_folds = tenfold_cross_val(raw_train_ds, checkpoint)\n",
        "cv_raw_data_cosine_similarity_avg = (sum(cv_raw_data_cosine_similarity_all_folds) / len(cv_raw_data_cosine_similarity_all_folds)) * 100"
      ],
      "metadata": {
        "id": "fkhDXCq5bn5n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_raw_data_cosine_similarity_avg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf09fae7-579f-4bef-fedb-52ca3eeb00ab",
        "id": "y1IfAdYubn5o"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77.78641879558563"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# roBERTa\n",
        "roberta-base  \n",
        "Cross Validation cosine_similarity = 77.61  \n",
        "Competition Test Set cosine_similarity = 82.18"
      ],
      "metadata": {
        "id": "u6H8aqRIpDMw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing for input into roBERTa"
      ],
      "metadata": {
        "id": "9yRLj7fKpHM4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "current = \"roberta-base\""
      ],
      "metadata": {
        "id": "X8cle69Rsewi"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(current)\n",
        "max_len_raw, max_len_token_vector = get_max_tokenized_length(raw_train_ds['title'] + raw_test_ds['title'], tok=tokenizer)\n",
        "\n",
        "print(f'For AutoTokenzier associated with \"{current}\":')\n",
        "print('(Note this will potentially be different for each pretrained model)\\n')\n",
        "print(f'max_len_raw (longest headline)= {max_len_raw}')\n",
        "print(f'max_len_tokenized (longest tokenized headline)= {max_len_token_vector}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f8385ec-c249-47b1-b62b-e4094302c264",
        "id": "-x9y1Px6sewj"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For AutoTokenzier associated with \"roberta-base\":\n",
            "(Note this will potentially be different for each pretrained model)\n",
            "\n",
            "max_len_raw (longest headline)= 18\n",
            "max_len_tokenized (longest tokenized headline)= 32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets, tf_train_dataset, tf_val_dataset, tf_test_dataset = create_tokenized_tf_datasets(untokenized_datasets_dict = raw_datasets, \n",
        "                                                                                                     tok = tokenizer, \n",
        "                                                                                                     max_len = max_len_token_vector)\n",
        "output.clear()"
      ],
      "metadata": {
        "id": "1yRBdc5Hsewj"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit and Evaluate RoBERTa"
      ],
      "metadata": {
        "id": "ytSHkaB6zem_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "load_compile_fit_eval_model(current, tf_train_dataset, tf_val_dataset, tf_test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485,
          "referenced_widgets": [
            "8e61a6708a4749778d39945c215b2911",
            "93cd1ddef46046739de54a1e8e11246b",
            "897b2574f97f4a5b9ebbb62f1efd6673",
            "616e234521aa44aeb0aee2d5d5ef6b28",
            "ecb52c2c422a4c15b0239d69caceed1c",
            "e26ab1bc3f924401ae9c2cc48e0652fb",
            "c1d3e202fdc545c781cabb6a623bd6d9",
            "6a367c9623a648dbb0ebc87edcd5c45b",
            "a7821edb09d54d0394959b043577d43d",
            "2b816a8f28a74748be9395a03072bece",
            "3931d79319c24a1198dbcd8406bbc09a"
          ]
        },
        "outputId": "01772239-550e-4079-bb0d-6efb42226ebd",
        "id": "yyPV44BDsewk"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"tf_model.h5\";:   0%|          | 0.00/657M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e61a6708a4749778d39945c215b2911"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFRobertaForSequenceClassification.\n",
            "\n",
            "Some layers of TFRobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "229/229 [==============================] - 33s 71ms/step - loss: 0.1448 - cosine_proximity: 0.2670 - val_loss: 0.0745 - val_cosine_proximity: 0.7281\n",
            "Epoch 2/50\n",
            "229/229 [==============================] - 14s 59ms/step - loss: 0.0824 - cosine_proximity: 0.6302 - val_loss: 0.0497 - val_cosine_proximity: 0.7105\n",
            "Epoch 3/50\n",
            "229/229 [==============================] - 13s 59ms/step - loss: 0.0520 - cosine_proximity: 0.7462 - val_loss: 0.0416 - val_cosine_proximity: 0.7456\n",
            "Epoch 4/50\n",
            "229/229 [==============================] - 14s 59ms/step - loss: 0.0396 - cosine_proximity: 0.7834 - val_loss: 0.0416 - val_cosine_proximity: 0.7281\n",
            "Epoch 5/50\n",
            "229/229 [==============================] - 14s 60ms/step - loss: 0.0330 - cosine_proximity: 0.7987 - val_loss: 0.0472 - val_cosine_proximity: 0.7632\n",
            "Epoch 6/50\n",
            "229/229 [==============================] - 14s 59ms/step - loss: 0.0299 - cosine_proximity: 0.8249 - val_loss: 0.0601 - val_cosine_proximity: 0.7368\n",
            "Epoch 7/50\n",
            "229/229 [==============================] - 14s 59ms/step - loss: 0.0294 - cosine_proximity: 0.8009 - val_loss: 0.0435 - val_cosine_proximity: 0.7368\n",
            "Epoch 8/50\n",
            "229/229 [==============================] - 14s 59ms/step - loss: 0.0266 - cosine_proximity: 0.8534 - val_loss: 0.0466 - val_cosine_proximity: 0.7368\n",
            "Epoch 9/50\n",
            "229/229 [==============================] - 14s 59ms/step - loss: 0.0217 - cosine_proximity: 0.8578 - val_loss: 0.0450 - val_cosine_proximity: 0.7193\n",
            "123/123 [==============================] - 6s 25ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8218125104904175"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perform 10-Fold Cross Validation\n",
        "(as a proxy for test set - prior to receiving ground truth test set)"
      ],
      "metadata": {
        "id": "lOeBrBigpHM6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv_raw_data_cosine_similarity_all_folds = tenfold_cross_val(raw_train_ds, checkpoint)\n",
        "cv_raw_data_cosine_similarity_avg = (sum(cv_raw_data_cosine_similarity_all_folds) / len(cv_raw_data_cosine_similarity_all_folds)) * 100"
      ],
      "metadata": {
        "id": "MpyCFIlSpHM7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_raw_data_cosine_similarity_avg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ouaFancpHM7",
        "outputId": "c514a5c9-acf9-4402-b50a-6dbe763cefc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77.60886251926422"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DeBERTa\n",
        "deberta-v3  \n",
        "Cross Validation cosine_similarity = 80.45  \n",
        "Competition Test Set cosine_similarity = 84.20"
      ],
      "metadata": {
        "id": "HSBzEFpJCwab"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "Ak1thg-iDgzo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall transformers\n",
        "!pip uninstall sentencepiece"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eN68qjJMrO_z",
        "outputId": "c83c47e0-153c-4b2b-bafc-cacf86a1ba16"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: transformers 4.26.0\n",
            "Uninstalling transformers-4.26.0:\n",
            "  Would remove:\n",
            "    /usr/local/bin/transformers-cli\n",
            "    /usr/local/lib/python3.8/dist-packages/transformers-4.26.0.dist-info/*\n",
            "    /usr/local/lib/python3.8/dist-packages/transformers/*\n",
            "Proceed (Y/n)? Y\n",
            "  Successfully uninstalled transformers-4.26.0\n",
            "\u001b[33mWARNING: Skipping sentencepiece as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[sentencepiece]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dGjWqxpGrDiy",
        "outputId": "44fa8d2d-6a0a-4efa-da88-2a3b55cd82e5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers[sentencepiece] in /usr/local/lib/python3.8/dist-packages (4.26.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (1.21.6)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (2022.6.2)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (0.13.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (3.9.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (4.64.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (0.12.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (2.25.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (6.0)\n",
            "Requirement already satisfied: protobuf<=3.20.2 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (3.19.6)\n",
            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (0.1.97)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers[sentencepiece]) (4.4.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers[sentencepiece]) (2022.12.7)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers[sentencepiece]) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers[sentencepiece]) (1.26.14)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers[sentencepiece]) (4.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing for input into DeBERTa"
      ],
      "metadata": {
        "id": "Xlzk8G9tNI2c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "current = \"microsoft/deberta-v3-base\""
      ],
      "metadata": {
        "id": "LR_BI9qLu-HO"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(current)\n",
        "max_len_raw, max_len_token_vector = get_max_tokenized_length(raw_train_ds['title'] + raw_test_ds['title'], tok=tokenizer)\n",
        "\n",
        "print(f'For AutoTokenzier associated with \"{current}\":')\n",
        "print('(Note this will potentially be different for each pretrained model)\\n')\n",
        "print(f'max_len_raw (longest headline)= {max_len_raw}')\n",
        "print(f'max_len_tokenized (longest tokenized headline)= {max_len_token_vector}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
            "463b765e84464d5a9020ab71ecb96097",
            "b6fd4de6f3b94f8e845dbae051ed5f02",
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            "18c5dfcf33b44da5a58fc46a981afdde"
          ]
        },
        "outputId": "62750501-01b3-4312-cb29-44c6ddc2a085",
        "id": "PBUyly9gu-HP"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "463b765e84464d5a9020ab71ecb96097"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/579 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "83155f87fb7843b2b326acb7ec1d3943"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"spm.model\";:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c8de8aa4f1944e78813025af6a03ff3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For AutoTokenzier associated with \"microsoft/deberta-v3-base\":\n",
            "(Note this will potentially be different for each pretrained model)\n",
            "\n",
            "max_len_raw (longest headline)= 18\n",
            "max_len_tokenized (longest tokenized headline)= 28\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets, tf_train_dataset, tf_val_dataset, tf_test_dataset = create_tokenized_tf_datasets(untokenized_datasets_dict = raw_datasets, \n",
        "                                                                                                     tok = tokenizer, \n",
        "                                                                                                     max_len = max_len_token_vector)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        },
        "id": "jHU41xoDu-HQ",
        "outputId": "a065a697-3412-4d62-d22b-63278c1baae8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e64533bb9c90434db8ae74e0cc53ee35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f669adbc48284ef587791803b6da42ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "895ab3f437334b368a613a4e6b3c09a1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit and Evaluate DeBERTa"
      ],
      "metadata": {
        "id": "eJlbTezzziTb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "load_compile_fit_eval_model(current, tf_train_dataset, tf_val_dataset, tf_test_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595,
          "referenced_widgets": [
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          ]
        },
        "outputId": "adad45ca-36fc-4d43-91f0-2a7ba3a09409",
        "id": "m_4Scg9Mu-HQ"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/371M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "55bf387d9a464ae0a46d6ce17f14e356"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDebertaV2ForSequenceClassification: ['mask_predictions.classifier.weight', 'lm_predictions.lm_head.dense.bias', 'mask_predictions.dense.bias', 'deberta.embeddings.position_embeddings.weight', 'lm_predictions.lm_head.LayerNorm.bias', 'mask_predictions.LayerNorm.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.bias', 'mask_predictions.LayerNorm.weight', 'lm_predictions.lm_head.dense.weight', 'mask_predictions.classifier.bias', 'mask_predictions.dense.weight']\n",
            "- This IS expected if you are initializing TFDebertaV2ForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDebertaV2ForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDebertaV2ForSequenceClassification were not initialized from the PyTorch model and are newly initialized: ['pooler.dense.weight', 'pooler.dense.bias', 'classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/transformers/models/deberta_v2/modeling_tf_deberta_v2.py:124: Bernoulli.__init__ (from tensorflow.python.ops.distributions.bernoulli) is deprecated and will be removed after 2019-01-01.\n",
            "Instructions for updating:\n",
            "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/bernoulli.py:86: Distribution.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\n",
            "Instructions for updating:\n",
            "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "229/229 [==============================] - 67s 142ms/step - loss: 0.1158 - cosine_proximity: 0.3895 - val_loss: 0.0700 - val_cosine_proximity: 0.6404\n",
            "Epoch 2/50\n",
            "229/229 [==============================] - 27s 118ms/step - loss: 0.0543 - cosine_proximity: 0.7221 - val_loss: 0.0524 - val_cosine_proximity: 0.7368\n",
            "Epoch 3/50\n",
            "229/229 [==============================] - 27s 117ms/step - loss: 0.0399 - cosine_proximity: 0.7724 - val_loss: 0.0525 - val_cosine_proximity: 0.7632\n",
            "Epoch 4/50\n",
            "229/229 [==============================] - 27s 117ms/step - loss: 0.0295 - cosine_proximity: 0.8315 - val_loss: 0.0625 - val_cosine_proximity: 0.7193\n",
            "Epoch 5/50\n",
            "229/229 [==============================] - 27s 117ms/step - loss: 0.0230 - cosine_proximity: 0.8249 - val_loss: 0.0777 - val_cosine_proximity: 0.6754\n",
            "Epoch 6/50\n",
            "229/229 [==============================] - 27s 117ms/step - loss: 0.0232 - cosine_proximity: 0.8337 - val_loss: 0.0604 - val_cosine_proximity: 0.6404\n",
            "Epoch 7/50\n",
            "229/229 [==============================] - 27s 117ms/step - loss: 0.0216 - cosine_proximity: 0.8381 - val_loss: 0.0660 - val_cosine_proximity: 0.7368\n",
            "Epoch 8/50\n",
            "229/229 [==============================] - 27s 117ms/step - loss: 0.0165 - cosine_proximity: 0.8425 - val_loss: 0.0603 - val_cosine_proximity: 0.7193\n",
            "123/123 [==============================] - 9s 29ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8420573472976685"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perform 10-Fold Cross Validation\n",
        "(as a proxy for test set - prior to receiving ground truth test set)"
      ],
      "metadata": {
        "id": "JMcFdzwDC9MN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv_raw_data_cosine_similarity_all_folds = tenfold_cross_val(raw_train_ds, checkpoint)\n",
        "cv_raw_data_cosine_similarity_avg = (sum(cv_raw_data_cosine_similarity_all_folds) / len(cv_raw_data_cosine_similarity_all_folds)) * 100"
      ],
      "metadata": {
        "id": "E3eqTT7oCiVo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv_raw_data_cosine_similarity_avg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1499bf9-88f9-41cc-bd87-c0410a2b779c",
        "id": "azBRfQ0BCiVp"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.44909358024597"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One-Time Validation Set Creation Step"
      ],
      "metadata": {
        "id": "z4exsw5yyBJg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "# Load the original .json file\n",
        "with open(\"data/finance/Headline_Trainingdata.json\", \"r\") as file:\n",
        "    original_data = json.load(file)\n",
        "\n",
        "# Select 228 random entries\n",
        "selected_entries = random.sample(original_data, 228)\n",
        "\n",
        "# Remove the selected entries from the original data\n",
        "for entry in selected_entries:\n",
        "    original_data.remove(entry)\n",
        "\n",
        "# Save the remaining entries in the original file\n",
        "with open(\"data/finance/Headline_Trainingdata.json\", \"w\") as file:\n",
        "    json.dump(original_data, file)\n",
        "\n",
        "# Save the selected entries to a new .json file\n",
        "with open(\"data/finance/Headline_Validationdata.json\", \"w\") as file:\n",
        "    json.dump(selected_entries, file)"
      ],
      "metadata": {
        "id": "SOrdrmLEyH5d"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}