Disso_NER_BERT_Cased.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-Tuning, Cross-Validation and PBT algorithm for NER-BERT\n",
        "\n",
        "Note: PBT algorithm took awhile to execute on colab pro"
      ],
      "metadata": {
        "id": "328j1o9Bvkc6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "All directories have been commented out and marked with a large text cell above where directories need to be replaced"
      ],
      "metadata": {
        "id": "Rni_GybxvV3V"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59sLnf1oQyC5",
        "outputId": "c6df3396-1fbc-4f1a-f8fd-8936328caec1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pickle5\n",
            "  Downloading pickle5-0.0.12-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (256 kB)\n",
            "\u001b[K     |████████████████████████████████| 256 kB 15.6 MB/s \n",
            "\u001b[?25hInstalling collected packages: pickle5\n",
            "Successfully installed pickle5-0.0.12\n"
          ]
        }
      ],
      "source": [
        "!pip install pickle5\n",
        "import pickle5 as pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5S3ymljVA8Ol"
      },
      "outputs": [],
      "source": [
        "!pip install datasets seqeval transformers \"ray[tune]\" scipy sklearn torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tcu3Ju-sPAmA",
        "outputId": "4352cd94-85ea-4840-d3fc-20defac94208"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datasets import Dataset, DatasetDict, load_dataset, load_metric\n",
        "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
        "from transformers import pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "import json\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import word_tokenize\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from transformers import DataCollatorForTokenClassification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hMFMx9pM7Kp6"
      },
      "outputs": [],
      "source": [
        "def tag_creator1(json):\n",
        "    data_list = json['annotations']\n",
        "    classes = json['classes']\n",
        "\n",
        "    store = []\n",
        "    for sent_ent in data_list:\n",
        "        store.append((sent_ent[0], sent_ent[1]))\n",
        "    \n",
        "    out = []\n",
        "    for words, ents in store:\n",
        "        letters = []\n",
        "        for letter in words:\n",
        "            letters.append(letter)\n",
        "        out.append((letters, ents))\n",
        "\n",
        "    #out = [(['T','o',' ','s'...],{'entities':[[118,132,'MIXED EFFECTS],[...],...]}),(....)]\n",
        "\n",
        "    all_ranges = []\n",
        "\n",
        "    for letters_list, entdict in out:\n",
        "        ranges=[]\n",
        "        for entpair in entdict['entities']:\n",
        "            ranges.append((entpair[0],entpair[1],entpair[2]))\n",
        "        all_ranges.append(ranges) # all_ranges is a list of lists where each sublist contains a tuple [(start_index, end_index, entity)] for each entdict\n",
        "\n",
        "    for letters_list_entdict_pair in out:\n",
        "        for ents in letters_list_entdict_pair[1]['entities']:\n",
        "            start = ents[0]\n",
        "            end = ents[1]\n",
        "            ent = ents[2]\n",
        "\n",
        "            for coded_pair in at_ent_tups:\n",
        "                if ent == coded_pair[1]:\n",
        "                    ent = coded_pair[0]\n",
        "\n",
        "            for i in range(start,end):\n",
        "                if letters_list_entdict_pair[0][i] != ' ':\n",
        "                  letters_list_entdict_pair[0][i] = ent\n",
        "\n",
        "    out2 = []\n",
        "    \n",
        "    for letters_list_entdict_pair in out:\n",
        "        new = \"\"\n",
        "        for letter in letters_list_entdict_pair[0]:\n",
        "            new += letter\n",
        "        out2.append((new, letters_list_entdict_pair[1]))\n",
        "\n",
        "    test = ['@0','@1','@2','@3','@4','@5','@6','@7']\n",
        "    testdict = {}\n",
        "    for number, cla in zip(test,classes):\n",
        "        testdict[number] = cla\n",
        "    #print(testdict)\n",
        "    #Tokenizing the words in each sentence\n",
        "    tagslist=[]\n",
        "    for sent, ents in out2:\n",
        "        tagslist.append(sent.split(\" \"))\n",
        "\n",
        "    #Below reassigns any non entities to 'O' and assigns any entities to 'B -' + the entity type\n",
        "    for tags in tagslist:\n",
        "        for i in range(len(tags)):\n",
        "            if '@0' in tags[i]:\n",
        "                tags[i] = 'B-' + testdict['@0']\n",
        "            elif '@1' in tags[i]:\n",
        "                tags[i] = 'B-' + testdict['@1']\n",
        "            elif '2@' in tags[i]:\n",
        "                tags[i] = 'B-' + testdict['@2']\n",
        "            elif '@3' in tags[i]:\n",
        "                tags[i] = 'B-' + testdict['@3']\n",
        "            elif '@4' in tags[i]:\n",
        "                tags[i] = 'B-' + testdict['@4']\n",
        "            elif '@5' in tags[i]:\n",
        "                tags[i] = 'B-' + testdict['@5']\n",
        "            elif '@6' in tags[i]:\n",
        "                tags[i] = 'B-' + testdict['@6']\n",
        "            elif '@7' in tags[i]:\n",
        "                tags[i] = 'B-' + testdict['@7']\n",
        "            else:\n",
        "                tags[i] = 'O'\n",
        "    #print(tagslist)\n",
        "\n",
        "\n",
        "    for tags in tagslist:\n",
        "        for index, elem in enumerate(tags):\n",
        "            el_minus_1 = tags[index-1]\n",
        "            el_current = tags[index]\n",
        "            if el_minus_1.startswith('B') and el_minus_1[-4:].endswith(el_current[-4:]):\n",
        "                tags[index] = tags[index].replace('B', 'I', 1)\n",
        "            elif el_minus_1.startswith('I') and el_minus_1[-4:].endswith(el_current[-4:]):\n",
        "              tags[index] = tags[index].replace('B','I', 1)\n",
        "\n",
        "\n",
        "    #print(tagslist)\n",
        "    return tagslist\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KzhaytUd6G-w"
      },
      "outputs": [],
      "source": [
        "def tags_convert2(tag_list):\n",
        "  out=[]\n",
        "  for tag in tag_list:\n",
        "    if tag == 'O':\n",
        "      out.append(0)\n",
        "    elif tag == 'B-OLS GLM':\n",
        "      out.append(1)\n",
        "    elif tag == 'I-OLS GLM':\n",
        "      out.append(2)\n",
        "    elif tag == 'B-MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)':\n",
        "      out.append(3)\n",
        "    elif tag == 'I-MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)':\n",
        "      out.append(4)\n",
        "    elif tag == 'B-BAYES FACTOR, BAYESIAN ESTIMATION':\n",
        "      out.append(5)\n",
        "    elif tag == 'I-BAYES FACTOR, BAYESIAN ESTIMATION':\n",
        "      out.append(6)\n",
        "    elif tag == 'B-NON-PARAMETRIC TESTS':\n",
        "      out.append(7)\n",
        "    elif tag == 'I-NON-PARAMETRIC TESTS':\n",
        "      out.append(8)\n",
        "    elif tag == 'B-ROBUST MODELS (BOOTSTRAPPED OLS, HCSES, M-ESTIMATORS, ROBUST TRIMMING)':\n",
        "      out.append(9)\n",
        "    elif tag == 'I-ROBUST MODELS (BOOTSTRAPPED OLS, HCSES, M-ESTIMATORS, ROBUST TRIMMING)':\n",
        "      out.append(10)\n",
        "    elif tag == 'B-SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA':\n",
        "      out.append(11)\n",
        "    elif tag == 'I-SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA':\n",
        "      out.append(12)\n",
        "    elif tag == 'B-OTHER':\n",
        "      out.append(13)\n",
        "    elif tag == 'I-OTHER':\n",
        "      out.append(14)\n",
        "    elif tag == 'B-CHI-SQUARE, FISHER EXACT':\n",
        "      out.append(15)\n",
        "    elif tag == 'I-CHI-SQUARE, FISHER EXACT':\n",
        "      out.append(16)\n",
        "    \n",
        "  return out"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **REPLACE DIRECTORY**"
      ],
      "metadata": {
        "id": "eD25uYWhtLVQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z2LD-vLnPGnG"
      },
      "outputs": [],
      "source": [
        "#Will need to replace directory here with own directory of JSON's in zip file. Order does not matter.\n",
        "f = open(#\"/content/drive/MyDrive/Colab Notebooks/anno_mart.json\")\n",
        "g = open(#\"/content/drive/MyDrive/Colab Notebooks/annotations_18_08_2022.json\")\n",
        "h = open(#\"/content/drive/MyDrive/Colab Notebooks/annotations_22_08_2022.json\")\n",
        "i = open(#\"/content/drive/MyDrive/Colab Notebooks/annotations_27_08_2022.json\")\n",
        "TRAIN_DATA = json.load(f)\n",
        "TRAIN_DATA1 = json.load(g)\n",
        "TRAIN_DATA2 = json.load(h)\n",
        "TRAIN_DATA3 = json.load(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AyqNEBqSWhRv",
        "outputId": "55b1f472-bdfc-450d-c15e-fff83de7bcbd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 'OLS GLM'),\n",
              " (1, 'MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)'),\n",
              " (2, 'BAYES FACTOR, BAYESIAN ESTIMATION'),\n",
              " (3, 'NON-PARAMETRIC TESTS'),\n",
              " (4, 'ROBUST MODELS (BOOTSTRAPPED OLS, HCSES, M-ESTIMATORS, ROBUST TRIMMING)'),\n",
              " (5, 'SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA'),\n",
              " (6, 'OTHER'),\n",
              " (7, 'CHI-SQUARE, FISHER EXACT'),\n",
              " (8, 'O')]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "coded_entity_types = list(enumerate(TRAIN_DATA['classes']))\n",
        "O = [(8, 'O')]\n",
        "coded_with_O = coded_entity_types + O\n",
        "coded_with_O"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cKiM1N95o5N",
        "outputId": "fbfaa801-ef52-4f8c-8546-d8c6b56cb50e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('@0', 'OLS GLM'),\n",
              " ('@1', 'MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)'),\n",
              " ('@2', 'BAYES FACTOR, BAYESIAN ESTIMATION'),\n",
              " ('@3', 'NON-PARAMETRIC TESTS'),\n",
              " ('@4',\n",
              "  'ROBUST MODELS (BOOTSTRAPPED OLS, HCSES, M-ESTIMATORS, ROBUST TRIMMING)'),\n",
              " ('@5', 'SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA'),\n",
              " ('@6', 'OTHER'),\n",
              " ('@7', 'CHI-SQUARE, FISHER EXACT')]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "at_ent_tups = []\n",
        "for i,j in coded_entity_types:\n",
        "  at_ent_tups.append(('@'+str(i),j))\n",
        "\n",
        "at_ent_tups"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DS7Rnw8aK7tA",
        "outputId": "771a036f-952b-4d6c-cc02-d3ca1e37c156"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['O',\n",
              " 'B-OLS GLM',\n",
              " 'I-OLS GLM',\n",
              " 'B-MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)',\n",
              " 'I-MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)',\n",
              " 'B-BAYES FACTOR, BAYESIAN ESTIMATION',\n",
              " 'I-BAYES FACTOR, BAYESIAN ESTIMATION',\n",
              " 'B-NON-PARAMETRIC TESTS',\n",
              " 'I-NON-PARAMETRIC TESTS',\n",
              " 'B-ROBUST MODELS (BOOTSTRAPPED OLS, HCSES, M-ESTIMATORS, ROBUST TRIMMING)',\n",
              " 'I-ROBUST MODELS (BOOTSTRAPPED OLS, HCSES, M-ESTIMATORS, ROBUST TRIMMING)',\n",
              " 'B-SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA',\n",
              " 'I-SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA',\n",
              " 'B-OTHER',\n",
              " 'I-OTHER',\n",
              " 'B-CHI-SQUARE, FISHER EXACT',\n",
              " 'I-CHI-SQUARE, FISHER EXACT']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "label_list=[]\n",
        "label_list.append(\"O\")\n",
        "for num,ent in coded_entity_types:\n",
        "  label_list.append(\"B-\"+ent)\n",
        "  label_list.append(\"I-\"+ent)\n",
        "label_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xAMVmBPbTrEp",
        "outputId": "26edae3e-5861-400a-837f-0b01b8bd561e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 'O'),\n",
              " (1, 'B-OLS GLM'),\n",
              " (2, 'I-OLS GLM'),\n",
              " (3, 'B-MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)'),\n",
              " (4, 'I-MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)'),\n",
              " (5, 'B-BAYES FACTOR, BAYESIAN ESTIMATION'),\n",
              " (6, 'I-BAYES FACTOR, BAYESIAN ESTIMATION'),\n",
              " (7, 'B-NON-PARAMETRIC TESTS'),\n",
              " (8, 'I-NON-PARAMETRIC TESTS'),\n",
              " (9,\n",
              "  'B-ROBUST MODELS (BOOTSTRAPPED OLS, HCSES, M-ESTIMATORS, ROBUST TRIMMING)'),\n",
              " (10,\n",
              "  'I-ROBUST MODELS (BOOTSTRAPPED OLS, HCSES, M-ESTIMATORS, ROBUST TRIMMING)'),\n",
              " (11, 'B-SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA'),\n",
              " (12, 'I-SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA'),\n",
              " (13, 'B-OTHER'),\n",
              " (14, 'I-OTHER'),\n",
              " (15, 'B-CHI-SQUARE, FISHER EXACT'),\n",
              " (16, 'I-CHI-SQUARE, FISHER EXACT')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "full_ent_codes = list(enumerate(label_list))\n",
        "full_ent_codes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **REPLACE DIRECTORY HERE** - for each dataframe assign the directory of one of the JSON's, order does not matter."
      ],
      "metadata": {
        "id": "gZlZh6EatTNn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ohOdMztdQaoe",
        "outputId": "1f940be6-cc90-4a2e-89ee-a45c0cd66218"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           sentences  \\\n",
              "0  To explore longitudinal trends in cognitive ab...   \n",
              "1  Regression slopes for all groups did not reach...   \n",
              "2  Within these analyses, the percent of particip...   \n",
              "3  Statistical differences across outcome groups ...   \n",
              "4  Because the population distribution of RVC is ...   \n",
              "\n",
              "                                            entities  \n",
              "0  {'entities': [[126, 157, 'MIXED EFFECTS, MLMS,...  \n",
              "1                 {'entities': [[0, 17, 'OLS GLM']]}  \n",
              "2  {'entities': [[210, 226, 'CHI-SQUARE, FISHER E...  \n",
              "3  {'entities': [[68, 104, 'OLS GLM'], [109, 141,...  \n",
              "4  {'entities': [[81, 100, 'NON-PARAMETRIC TESTS']]}  "
            ],
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              "      <th>0</th>\n",
              "      <td>To explore longitudinal trends in cognitive ab...</td>\n",
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              "      <td>Regression slopes for all groups did not reach...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Within these analyses, the percent of particip...</td>\n",
              "      <td>{'entities': [[210, 226, 'CHI-SQUARE, FISHER E...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Statistical differences across outcome groups ...</td>\n",
              "      <td>{'entities': [[68, 104, 'OLS GLM'], [109, 141,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Because the population distribution of RVC is ...</td>\n",
              "      <td>{'entities': [[81, 100, 'NON-PARAMETRIC TESTS']]}</td>\n",
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        "df = pd.read_json(#\"/content/drive/MyDrive/Colab Notebooks/anno_mart.json\", orient='index' )\n",
        "df = df.iloc[1]\n",
        "sentences=[]\n",
        "entity_dicts=[]\n",
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        "  sentences.append(i[0])\n",
        "  entity_dicts.append(i[1])\n",
        "\n",
        "new = {\"sentences\": sentences, \"entities\": entity_dicts}\n",
        "\n",
        "df = pd.DataFrame(new)\n",
        "df.head()"
      ]
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              "                                             sentences  \\\n",
              "98   For any other MHP, the logistic regression mod...   \n",
              "99   Cox regression was used to compare the risk of...   \n",
              "100  The differences between this group and the sta...   \n",
              "101  This subscale included three items namely, “su...   \n",
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              "100  {'entities': [[115, 136, 'NON-PARAMETRIC TESTS...  \n",
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        "dfx = pd.read_json(#\"/content/drive/MyDrive/Colab Notebooks/annotations_18_08_2022.json\", orient='index')\n",
        "dfx = dfx.iloc[1]\n",
        "sentences=[]\n",
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        "dfx.tail()"
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              "                                            sentences  \\\n",
              "87  We followed an iterative process to build the ...   \n",
              "88  Adolescent sex was included in all hierarchica...   \n",
              "89  The pre-treatment score for the outcome variab...   \n",
              "90  Logistic regression analyses were conducted to...   \n",
              "91  A chi-square analysis revealed that the differ...   \n",
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              "                                             entities  \n",
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              "      <th>90</th>\n",
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              "    </tr>\n",
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              "      <th>91</th>\n",
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              "      <td>{'entities': [[2, 21, 'CHI-SQUARE, FISHER EXAC...</td>\n",
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              "\n",
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          "metadata": {},
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      ],
      "source": [
        "dfy = pd.read_json(#\"/content/drive/MyDrive/Colab Notebooks/annotations_22_08_2022.json\", orient='index')\n",
        "dfy = dfy.iloc[1]\n",
        "sentences=[]\n",
        "entity_dicts=[]\n",
        "for i in dfy:\n",
        "  sentences.append(i[0])\n",
        "  entity_dicts.append(i[1])\n",
        "\n",
        "new = {\"sentences\": sentences, \"entities\": entity_dicts}\n",
        "\n",
        "dfy = pd.DataFrame(new)\n",
        "dfy.tail()"
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    {
      "cell_type": "code",
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        "dfz = pd.read_json(#\"/content/drive/MyDrive/Colab Notebooks/annotations_27_08_2022.json\", orient='index')\n",
        "dfz = dfz.iloc[1]\n",
        "sentences=[]\n",
        "entity_dicts=[]\n",
        "for i in dfz:\n",
        "  sentences.append(i[0])\n",
        "  entity_dicts.append(i[1])\n",
        "\n",
        "new = {\"sentences\": sentences, \"entities\": entity_dicts}\n",
        "\n",
        "dfz = pd.DataFrame(new)\n",
        "dfz.tail()"
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          "height": 206
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "                                             sentences  \\\n",
              "297  n the current study, a new 2-factor analysis o...   \n",
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              "300  {'entities': [[57, 72, 'SCALE DEVELOPMENT, FAC...  \n",
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              "                                             sentences  \\\n",
              "0    To explore longitudinal trends in cognitive ab...   \n",
              "1    Regression slopes for all groups did not reach...   \n",
              "2    Within these analyses, the percent of particip...   \n",
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              "..                                                 ...   \n",
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      ],
      "source": [
        "df = pd.concat([df,dfx,dfy,dfz],ignore_index=True)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5JuVDKh3ALT1"
      },
      "outputs": [],
      "source": [
        "tags = tag_creator1(TRAIN_DATA)\n",
        "tagsx = tag_creator1(TRAIN_DATA1)\n",
        "tagsy = tag_creator1(TRAIN_DATA2)\n",
        "tagsz = tag_creator1(TRAIN_DATA3)\n",
        "tags1 = []\n",
        "for i in tags:\n",
        "  tags1.append(tags_convert2(i))\n",
        "\n",
        "for i in tagsx:\n",
        "  tags1.append(tags_convert2(i))\n",
        "\n",
        "for i in tagsy:\n",
        "  tags1.append(tags_convert2(i))\n",
        "\n",
        "for i in tagsz:\n",
        "  tags1.append(tags_convert2(i))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XUrLCZuaUXRh"
      },
      "outputs": [],
      "source": [
        "sentence_taglist_dict = {}\n",
        "for sent, tags in zip(df['sentences'],tags1):\n",
        "  sentence_taglist_dict[sent] = tags"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S2c2QFQg_BGB"
      },
      "outputs": [],
      "source": [
        "def label(row):\n",
        "  for i in df['sentences']:\n",
        "      if row == i:\n",
        "        return sentence_taglist_dict[i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jFS2rIvL_2Qh"
      },
      "outputs": [],
      "source": [
        "df['Coded'] = df['sentences'].apply(lambda row: label(row))"
      ]
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    {
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            "text/plain": [
              "                                             sentences  \\\n",
              "0    [To, explore, longitudinal, trends, in, cognit...   \n",
              "1    [Regression, slopes, for, all, groups, did, no...   \n",
              "2    [Within, these, analyses,, the, percent, of, p...   \n",
              "3    [Statistical, differences, across, outcome, gr...   \n",
              "4    [Because, the, population, distribution, of, R...   \n",
              "..                                                 ...   \n",
              "307  [We, followed, an, iterative, process, to, bui...   \n",
              "308  [Adolescent, sex, was, included, in, all, hier...   \n",
              "309  [The, pre-treatment, score, for, the, outcome,...   \n",
              "310  [Logistic, regression, analyses, were, conduct...   \n",
              "311  [A, chi-square, analysis, revealed, that, the,...   \n",
              "\n",
              "                                              entities  \\\n",
              "0    {'entities': [[126, 157, 'MIXED EFFECTS, MLMS,...   \n",
              "1                   {'entities': [[0, 17, 'OLS GLM']]}   \n",
              "2    {'entities': [[210, 226, 'CHI-SQUARE, FISHER E...   \n",
              "3    {'entities': [[68, 104, 'OLS GLM'], [109, 141,...   \n",
              "4    {'entities': [[81, 100, 'NON-PARAMETRIC TESTS']]}   \n",
              "..                                                 ...   \n",
              "307  {'entities': [[46, 66, 'MIXED EFFECTS, MLMS, G...   \n",
              "308  {'entities': [[35, 67, 'OLS GLM'], [72, 104, '...   \n",
              "309  {'entities': [[72, 91, 'OLS GLM'], [126, 150, ...   \n",
              "310  {'entities': [[0, 28, 'MIXED EFFECTS, MLMS, GR...   \n",
              "311  {'entities': [[2, 21, 'CHI-SQUARE, FISHER EXAC...   \n",
              "\n",
              "                                                 Coded  \n",
              "0    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "1    [1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "2    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "3    [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 0, 15,...  \n",
              "4    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, ...  \n",
              "..                                                 ...  \n",
              "307  [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, ...  \n",
              "308  [0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 2, 2, 2, 0, ...  \n",
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              "      <th>308</th>\n",
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              "<p>312 rows × 3 columns</p>\n",
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              "          const element = document.querySelector('#df-ed2ccda9-5678-46f3-ad0b-6dbb877f2ccf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df['sentences'] = df['sentences'].apply(lambda row: row.split(\" \"))\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9o5vVTegUTXu"
      },
      "outputs": [],
      "source": [
        "df = df.drop('entities',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "T6zMEyngVAw0",
        "outputId": "f56a4af2-1a62-4dfe-ba4b-02d05026852a"
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      "outputs": [
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              "                                             Sentences  \\\n",
              "0    [To, explore, longitudinal, trends, in, cognit...   \n",
              "1    [Regression, slopes, for, all, groups, did, no...   \n",
              "2    [Within, these, analyses,, the, percent, of, p...   \n",
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              "..                                                 ...   \n",
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              "\n",
              "                                                 Coded  \n",
              "0    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
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              "4    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, ...  \n",
              "..                                                 ...  \n",
              "307  [0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 4, 0, 0, 0, 0, ...  \n",
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              "310  [3, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
              "311  [0, 15, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...  \n",
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              "      <td>[To, explore, longitudinal, trends, in, cognit...</td>\n",
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              "      <td>[Regression, slopes, for, all, groups, did, no...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[Within, these, analyses,, the, percent, of, p...</td>\n",
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              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>[Adolescent, sex, was, included, in, all, hier...</td>\n",
              "      <td>[0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 2, 2, 2, 0, ...</td>\n",
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              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>[The, pre-treatment, score, for, the, outcome,...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>310</th>\n",
              "      <td>[Logistic, regression, analyses, were, conduct...</td>\n",
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              "    <tr>\n",
              "      <th>311</th>\n",
              "      <td>[A, chi-square, analysis, revealed, that, the,...</td>\n",
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              "<p>312 rows × 2 columns</p>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a457633a-403f-405d-a934-471f72c687eb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df = df.rename(columns={\"sentences\": \"Sentences\"})\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xfMsKPjeoGlc"
      },
      "outputs": [],
      "source": [
        "#tds = Dataset.from_pandas(train)\n",
        "#tsds = Dataset.from_pandas(test)\n",
        "#vds = Dataset.from_pandas(validate)\n",
        "#ds = DatasetDict()\n",
        "#ds['train'] = tds\n",
        "#ds['test'] = tsds\n",
        "#ds['validate'] = vds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "438nVXagPLW7",
        "outputId": "db32aa25-ed77-4911-dd69-9aefe9a866d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Sentences', 'Coded'],\n",
              "        num_rows: 249\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Sentences', 'Coded'],\n",
              "        num_rows: 63\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "ds = Dataset.from_pandas(df).train_test_split(test_size=0.2)\n",
        "ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "QHCgVqGtPEBT",
        "outputId": "d82f6f64-8d6d-4d85-97b6-3be0c874e154"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading config.json:   0%|          | 0.00/829 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b458a4f2474b47179130dbb8a1bc18cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/413M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4fb79362a13b499fa8c05873f785ccdf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at dslim/bert-base-NER and are newly initialized because the shapes did not match:\n",
            "- classifier.weight: found shape torch.Size([9, 768]) in the checkpoint and torch.Size([17, 768]) in the model instantiated\n",
            "- classifier.bias: found shape torch.Size([9]) in the checkpoint and torch.Size([17]) in the model instantiated\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": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer_config.json:   0%|          | 0.00/59.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b38e4f768034f6cafac03e8af101d34"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading vocab.txt:   0%|          | 0.00/208k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b2def7d02564006b795ba61118ff59c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading added_tokens.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed30f436c1a24d03935191563e919c12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "018a15c5894d4b8cbc11d1ec6910a9f1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\", num_labels=len(label_list), ignore_mismatched_sizes=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OqLvJdJeTJlx"
      },
      "outputs": [],
      "source": [
        "label_all_tokens = True\n",
        "\n",
        "def tokenize_and_align_labels(examples):\n",
        "    tokenized_inputs = tokenizer(examples[\"Sentences\"], truncation=True, is_split_into_words=True)\n",
        "\n",
        "    labels = []\n",
        "    for i, label in enumerate(examples[\"Coded\"]):\n",
        "        word_ids = tokenized_inputs.word_ids(batch_index=i)\n",
        "        previous_word_idx = None\n",
        "        label_ids = []\n",
        "        for word_idx in word_ids:\n",
        "            # Special tokens have a word id that is None. We set the label to -100 so they are automatically\n",
        "            # ignored in the loss function.\n",
        "            if word_idx is None:\n",
        "                label_ids.append(-100)\n",
        "            # We set the label for the first token of each word.\n",
        "            elif word_idx != previous_word_idx:\n",
        "                label_ids.append(label[word_idx])\n",
        "            # For the other tokens in a word, we set the label to either the current label or -100, depending on\n",
        "            # the label_all_tokens flag.\n",
        "            else:\n",
        "                label_ids.append(label[word_idx] if label_all_tokens else -100)\n",
        "            previous_word_idx = word_idx\n",
        "\n",
        "        labels.append(label_ids)\n",
        "\n",
        "    tokenized_inputs[\"labels\"] = labels\n",
        "    return tokenized_inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "yPPxl51uM6fh",
        "outputId": "64629cf1-ebc4-477e-c7a1-bbfb8b8ccb02"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e37558bdea1a45738cd4be6ae6770ca3"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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      ],
      "source": [
        "tokenized_datasets = ds.map(tokenize_and_align_labels, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "id": "BIXD9Z07Bkv6",
        "outputId": "6183898b-a12a-42ae-af2f-7a89b8d04080"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41db49d101be41258ad14cb11f845b49"
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          "metadata": {}
        }
      ],
      "source": [
        "batch_size=8\n",
        "\n",
        "args = TrainingArguments(\n",
        "    \"nerbert-finetune\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=20,\n",
        "    weight_decay=0.01,\n",
        "    logging_steps=1\n",
        ")\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer)\n",
        "\n",
        "metric = load_metric(\"seqeval\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pRFr68fSTnbF"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "    # Remove ignored index (special tokens)\n",
        "    true_predictions = [\n",
        "        [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "    true_labels = [\n",
        "        [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    results = metric.compute(predictions=true_predictions, references=true_labels, zero_division=1)\n",
        "    return {\n",
        "        \"precision\": results[\"overall_precision\"],\n",
        "        \"recall\": results[\"overall_recall\"],\n",
        "        \"f1\": results[\"overall_f1\"],\n",
        "        \"accuracy\": results[\"overall_accuracy\"],\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YcoRXf6Bugg2"
      },
      "outputs": [],
      "source": [
        "def model_init():\n",
        "    return AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\", num_labels=len(label_list), ignore_mismatched_sizes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Wd9pWZTTtLE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7192c555-d239-4eaa-86ef-f6582367ceae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/trainer.py:350: FutureWarning: `Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will overwrite your model when calling the `train` method. This will become a fatal error in the next release.\n",
            "  FutureWarning,\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "    model_init = model_init\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#PBT"
      ],
      "metadata": {
        "id": "2fdYsOoFDeNP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GG6Xm8LsVdeW"
      },
      "outputs": [],
      "source": [
        "import ray\n",
        "from ray import tune\n",
        "from ray.tune.schedulers import PopulationBasedTraining\n",
        "from ray.tune import CLIReporter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pTaUc2sRiFP7"
      },
      "outputs": [],
      "source": [
        "tune_config = {\n",
        "    \"per_device_train_batch_size\": 32,\n",
        "    \"per_device_eval_batch_size\": 32,\n",
        "    \"num_train_epochs\": tune.choice([10, 15]),\n",
        "    #\"max_steps\": 1 if smoke_test else -1,  # Used for smoke test.\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n7725UL8Uk8a"
      },
      "outputs": [],
      "source": [
        "scheduler = PopulationBasedTraining(\n",
        "    time_attr=\"training_iteration\",\n",
        "    metric=\"eval_f1\",\n",
        "    mode=\"max\",\n",
        "    perturbation_interval=1,\n",
        "    hyperparam_mutations={\n",
        "      \"weight_decay\": tune.uniform(0.0, 0.3), \n",
        "      \"learning_rate\": tune.uniform(1e-5, 5e-5),\n",
        "      \"per_device_train_batch_size\": [16, 32, 64]\n",
        "    }\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QaIBXuQCjBfn"
      },
      "outputs": [],
      "source": [
        "reporter = CLIReporter(\n",
        "    parameter_columns={\n",
        "        \"weight_decay\": \"w_decay\",\n",
        "        \"learning_rate\": \"lr\",\n",
        "        \"per_device_train_batch_size\": \"train_bs/gpu\",\n",
        "        \"num_train_epochs\": \"num_epochs\",\n",
        "    },\n",
        "    metric_columns=[\"eval_f1\", \"eval_loss\", \"epoch\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EpsEDvS2QWyq"
      },
      "outputs": [],
      "source": [
        "hpsearch = trainer.hyperparameter_search(\n",
        "    hp_space=lambda _: tune_config,\n",
        "    backend=\"ray\",\n",
        "    n_trials=5, # number of trials\n",
        "    scheduler=scheduler,\n",
        "    resources_per_trial={\"cpu\": 2, \"gpu\": 1},\n",
        "    keep_checkpoints_num=1,\n",
        "    checkpoint_score_attr=\"training_iteration\",\n",
        "    progress_reporter=reporter,\n",
        "    reuse_actors=True,\n",
        "    local_dir=\"/content/drive/MyDrive/Colab Notebooks/\",\n",
        "    name=\"tune_transformer_pbt_ner-bert\",\n",
        "    log_to_file=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "cAQM2QrhP3qD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CV"
      ],
      "metadata": {
        "id": "-oAULGjrP9q_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#4-Fold psuedo CV\n",
        "ds1 = Dataset.from_pandas(df).train_test_split(test_size=0.2, seed=1)\n",
        "ds2 = Dataset.from_pandas(df).train_test_split(test_size=0.2, seed=2)\n",
        "ds3 = Dataset.from_pandas(df).train_test_split(test_size=0.2, seed=3)\n",
        "ds4 = Dataset.from_pandas(df).train_test_split(test_size=0.2, seed=4)\n",
        "tds1 = ds1.map(tokenize_and_align_labels, batched=True)\n",
        "tds2 = ds2.map(tokenize_and_align_labels, batched=True)\n",
        "tds3 = ds3.map(tokenize_and_align_labels, batched=True)\n",
        "tds4 = ds4.map(tokenize_and_align_labels, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
            "fa4e855607df4e86aed4102914393445",
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            "05017bed248541bab931c11b1736cce8",
            "deebeb12695046acbecdaba541712a73",
            "ba0cce3567cd406a9a6ec9b228a4ae70",
            "941ef136843d42ab99c24190ae831ee8",
            "c1ba25f6c1e049c8bc5811f1e864186b",
            "449a0eb8b8c14b2a801c418ecff2f763",
            "3c7089e1f529462d9de09e4e9c71afb4",
            "eb779504ef524a5a9f8f0a2ab77a81e4",
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            "d8706a64bcd04a53a1e3cc058ed87f77",
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            "972630cbccdc457285c07be324bfb3ab",
            "7611b02df8824a51b63650075faef5d4",
            "5b48da6fc4e34e51bb07f4cf711f33f5",
            "e7c331065da049baa4e19b466b0ea20e",
            "2d35d18be7944aff93e2ccb5fea058f9",
            "40149901068b48c794aa3d5e21c38c34",
            "c30c75342ade4ce38b253b9627a59d26",
            "87f391e75002495c9f10cde20c5554a8",
            "f7d7d45ff83e44c5ba6311433121e392",
            "8bdadf7d23b546fbb6e586807ce70e16",
            "16b487efeca34e92b28ca8c411c64b04",
            "5e9959e8558e4ea99cf53b758a3ae2c8",
            "695a86fe18d34123bce03c25374d25e7",
            "4c3ddcda2b3a4f3e812768e6d98ff74c",
            "28071d53a18a403281352464c87d8445",
            "71acdca86d6c4bcbaa59f5d1d0b495ba",
            "0ddc7a2c1f3949d4ab83f6ac31426222"
          ]
        },
        "id": "vAActV3uvlYy",
        "outputId": "20bbcaa7-dc98-4a1e-fa81-b355194fdad7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "args = TrainingArguments(\n",
        "    \"nerbert-finetune\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=3.888e-05,\n",
        "    per_device_train_batch_size=32,\n",
        "    per_device_eval_batch_size=32,\n",
        "    num_train_epochs=20,\n",
        "    weight_decay=0.0169235,\n",
        "    logging_steps=1   \n",
        ")"
      ],
      "metadata": {
        "id": "QSeK17mBb_iB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40181243-337f-4964-a543-3d917bbb1a6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fold 1\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\", num_labels=len(label_list), ignore_mismatched_sizes=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tds1[\"train\"],\n",
        "    eval_dataset=tds1[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "    #model_init = model_init\n",
        ")\n",
        "trainer.train()\n",
        "\n",
        "trainer.evaluate()\n",
        "\n",
        "predictions, labels, _ = trainer.predict(tds1[\"test\"])\n",
        "predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "true_predictions = [\n",
        "    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "true_labels = [\n",
        "    [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "\n",
        "results_fold1 = metric.compute(predictions=true_predictions, references=true_labels, zero_division=1)\n",
        "print(results_fold1)\n",
        "\n",
        "#Fold 2\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\", num_labels=len(label_list), ignore_mismatched_sizes=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tds2[\"train\"],\n",
        "    eval_dataset=tds2[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "    #model_init = model_init\n",
        ")\n",
        "trainer.train()\n",
        "\n",
        "trainer.evaluate()\n",
        "\n",
        "predictions, labels, _ = trainer.predict(tds2[\"test\"])\n",
        "predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "true_predictions = [\n",
        "    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "true_labels = [\n",
        "    [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "\n",
        "results_fold2 = metric.compute(predictions=true_predictions, references=true_labels, zero_division=1)\n",
        "print(results_fold2)\n",
        "\n",
        "#Fold 3\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\", num_labels=len(label_list), ignore_mismatched_sizes=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tds3[\"train\"],\n",
        "    eval_dataset=tds3[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "    #model_init = model_init\n",
        ")\n",
        "trainer.train()\n",
        "\n",
        "trainer.evaluate()\n",
        "\n",
        "predictions, labels, _ = trainer.predict(tds3[\"test\"])\n",
        "predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "true_predictions = [\n",
        "    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "true_labels = [\n",
        "    [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "\n",
        "results_fold3 = metric.compute(predictions=true_predictions, references=true_labels, zero_division=1)\n",
        "print(results_fold3)\n",
        "#Fold 4\n",
        "model = AutoModelForTokenClassification.from_pretrained(\"dslim/bert-base-NER\", num_labels=len(label_list), ignore_mismatched_sizes=True)\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"dslim/bert-base-NER\")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=tds4[\"train\"],\n",
        "    eval_dataset=tds4[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics,\n",
        "    #model_init = model_init\n",
        ")\n",
        "trainer.train()\n",
        "\n",
        "trainer.evaluate()\n",
        "\n",
        "predictions, labels, _ = trainer.predict(tds4[\"test\"])\n",
        "predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "true_predictions = [\n",
        "    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "true_labels = [\n",
        "    [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "\n",
        "results_fold4 = metric.compute(predictions=true_predictions, references=true_labels, zero_division=1)\n",
        "print(results_fold4)"
      ],
      "metadata": {
        "id": "ny1Cam1KXesB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(results_fold1)\n",
        "print(results_fold2)\n",
        "print(results_fold3)\n",
        "print(results_fold4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vhm3La8FdFgL",
        "outputId": "c438e92d-4491-48ed-eee9-b04facbae827"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'CHI-SQUARE, FISHER EXACT': {'precision': 0.47058823529411764, 'recall': 0.6153846153846154, 'f1': 0.5333333333333333, 'number': 13}, 'MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)': {'precision': 0.4666666666666667, 'recall': 0.7368421052631579, 'f1': 0.5714285714285714, 'number': 19}, 'NON-PARAMETRIC TESTS': {'precision': 0.5862068965517241, 'recall': 0.85, 'f1': 0.6938775510204082, 'number': 20}, 'OLS GLM': {'precision': 0.6030534351145038, 'recall': 0.7117117117117117, 'f1': 0.652892561983471, 'number': 111}, 'OTHER': {'precision': 1.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}, 'SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}, 'overall_precision': 0.5539906103286385, 'overall_recall': 0.6483516483516484, 'overall_f1': 0.5974683544303798, 'overall_accuracy': 0.9528432732316228}\n",
            "{'CHI-SQUARE, FISHER EXACT': {'precision': 0.5454545454545454, 'recall': 1.0, 'f1': 0.7058823529411764, 'number': 6}, 'MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)': {'precision': 0.5428571428571428, 'recall': 0.59375, 'f1': 0.5671641791044776, 'number': 32}, 'NON-PARAMETRIC TESTS': {'precision': 0.2727272727272727, 'recall': 0.2857142857142857, 'f1': 0.2790697674418604, 'number': 21}, 'OLS GLM': {'precision': 0.5925925925925926, 'recall': 0.6597938144329897, 'f1': 0.624390243902439, 'number': 97}, 'OTHER': {'precision': 1.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}, 'SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA': {'precision': 0.4444444444444444, 'recall': 0.2962962962962963, 'f1': 0.3555555555555555, 'number': 27}, 'overall_precision': 0.5309278350515464, 'overall_recall': 0.5567567567567567, 'overall_f1': 0.5435356200527705, 'overall_accuracy': 0.9384333246004716}\n",
            "{'CHI-SQUARE, FISHER EXACT': {'precision': 0.8461538461538461, 'recall': 0.4230769230769231, 'f1': 0.5641025641025641, 'number': 26}, 'MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)': {'precision': 0.6153846153846154, 'recall': 0.6857142857142857, 'f1': 0.6486486486486486, 'number': 35}, 'NON-PARAMETRIC TESTS': {'precision': 0.5652173913043478, 'recall': 0.5416666666666666, 'f1': 0.5531914893617021, 'number': 24}, 'OLS GLM': {'precision': 0.6228070175438597, 'recall': 0.6016949152542372, 'f1': 0.6120689655172413, 'number': 118}, 'SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA': {'precision': 0.5, 'recall': 0.75, 'f1': 0.6, 'number': 4}, 'overall_precision': 0.6256410256410256, 'overall_recall': 0.5893719806763285, 'overall_f1': 0.6069651741293532, 'overall_accuracy': 0.9506437768240343}\n",
            "{'CHI-SQUARE, FISHER EXACT': {'precision': 0.16666666666666666, 'recall': 0.4, 'f1': 0.23529411764705882, 'number': 5}, 'MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)': {'precision': 0.675, 'recall': 0.6585365853658537, 'f1': 0.6666666666666667, 'number': 41}, 'NON-PARAMETRIC TESTS': {'precision': 0.46153846153846156, 'recall': 0.6, 'f1': 0.5217391304347826, 'number': 10}, 'OLS GLM': {'precision': 0.6633663366336634, 'recall': 0.6442307692307693, 'f1': 0.6536585365853659, 'number': 104}, 'OTHER': {'precision': 1.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}, 'SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA': {'precision': 0.5263157894736842, 'recall': 0.5555555555555556, 'f1': 0.5405405405405405, 'number': 18}, 'overall_precision': 0.6054054054054054, 'overall_recall': 0.6222222222222222, 'overall_f1': 0.6136986301369863, 'overall_accuracy': 0.9536859413537956}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "folds = [results_fold1, results_fold2, results_fold3, results_fold4]\n",
        "CV_F1S = [i['overall_f1'] for i in folds]\n",
        "CV_P = [i['overall_precision'] for i in folds]\n",
        "CV_R = [i['overall_recall'] for i in folds]\n",
        "\n",
        "print(\"Mean F1 across folds: {}\".format(np.mean(CV_F1S)))\n",
        "print(\"F1 Std across folds: {}\".format(np.std(CV_F1S)))\n",
        "print(\"Mean Precision across folds: {}\".format(np.mean(CV_P)))\n",
        "print(\"Precision Std across folds: {}\".format(np.std(CV_P)))\n",
        "print(\"Mean Recall across folds: {}\".format(np.mean(CV_R)))\n",
        "print(\"Recall Std across folds: {}\".format(np.std(CV_R)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQxUbb_Fd1xR",
        "outputId": "f9902bf0-88a7-4a32-f18f-125c2dcb4d58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean F1 across folds: 0.5904169446873725\n",
            "F1 Std across folds: 0.02767427394665451\n",
            "Mean Precision across folds: 0.578991219106654\n",
            "Precision Std across folds: 0.03810850631295987\n",
            "Mean Recall across folds: 0.604175652001739\n",
            "Recall Std across folds: 0.034441608779191134\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cI1o6IyoYWl7",
        "outputId": "3d510ecf-8dab-4451-eab6-77dc0f0dcc92"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 176\n",
            "  Num Epochs = 20\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 440\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='440' max='440' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [440/440 1:20:38, Epoch 20/20]\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",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.438800</td>\n",
              "      <td>0.550705</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.038168</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.890625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.391500</td>\n",
              "      <td>0.396569</td>\n",
              "      <td>0.113636</td>\n",
              "      <td>0.076336</td>\n",
              "      <td>0.091324</td>\n",
              "      <td>0.897837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.491600</td>\n",
              "      <td>0.321390</td>\n",
              "      <td>0.188406</td>\n",
              "      <td>0.198473</td>\n",
              "      <td>0.193309</td>\n",
              "      <td>0.911859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.259500</td>\n",
              "      <td>0.292525</td>\n",
              "      <td>0.245902</td>\n",
              "      <td>0.229008</td>\n",
              "      <td>0.237154</td>\n",
              "      <td>0.917869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.189500</td>\n",
              "      <td>0.287712</td>\n",
              "      <td>0.245033</td>\n",
              "      <td>0.282443</td>\n",
              "      <td>0.262411</td>\n",
              "      <td>0.912660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.082700</td>\n",
              "      <td>0.272262</td>\n",
              "      <td>0.369863</td>\n",
              "      <td>0.412214</td>\n",
              "      <td>0.389892</td>\n",
              "      <td>0.923878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.164200</td>\n",
              "      <td>0.278888</td>\n",
              "      <td>0.414634</td>\n",
              "      <td>0.389313</td>\n",
              "      <td>0.401575</td>\n",
              "      <td>0.930288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.267801</td>\n",
              "      <td>0.462069</td>\n",
              "      <td>0.511450</td>\n",
              "      <td>0.485507</td>\n",
              "      <td>0.936699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.020200</td>\n",
              "      <td>0.262895</td>\n",
              "      <td>0.526316</td>\n",
              "      <td>0.534351</td>\n",
              "      <td>0.530303</td>\n",
              "      <td>0.937500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.074300</td>\n",
              "      <td>0.272992</td>\n",
              "      <td>0.510638</td>\n",
              "      <td>0.549618</td>\n",
              "      <td>0.529412</td>\n",
              "      <td>0.937500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.019600</td>\n",
              "      <td>0.269729</td>\n",
              "      <td>0.569343</td>\n",
              "      <td>0.595420</td>\n",
              "      <td>0.582090</td>\n",
              "      <td>0.942308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.046500</td>\n",
              "      <td>0.285992</td>\n",
              "      <td>0.541353</td>\n",
              "      <td>0.549618</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.940304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.024100</td>\n",
              "      <td>0.292903</td>\n",
              "      <td>0.528571</td>\n",
              "      <td>0.564885</td>\n",
              "      <td>0.546125</td>\n",
              "      <td>0.938301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.026600</td>\n",
              "      <td>0.297819</td>\n",
              "      <td>0.521739</td>\n",
              "      <td>0.549618</td>\n",
              "      <td>0.535316</td>\n",
              "      <td>0.937099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.305167</td>\n",
              "      <td>0.529412</td>\n",
              "      <td>0.549618</td>\n",
              "      <td>0.539326</td>\n",
              "      <td>0.938702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.012200</td>\n",
              "      <td>0.313498</td>\n",
              "      <td>0.579365</td>\n",
              "      <td>0.557252</td>\n",
              "      <td>0.568093</td>\n",
              "      <td>0.940705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.006500</td>\n",
              "      <td>0.307445</td>\n",
              "      <td>0.521127</td>\n",
              "      <td>0.564885</td>\n",
              "      <td>0.542125</td>\n",
              "      <td>0.939103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.005300</td>\n",
              "      <td>0.309029</td>\n",
              "      <td>0.543478</td>\n",
              "      <td>0.572519</td>\n",
              "      <td>0.557621</td>\n",
              "      <td>0.940304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.048200</td>\n",
              "      <td>0.309202</td>\n",
              "      <td>0.539568</td>\n",
              "      <td>0.572519</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.939904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.033900</td>\n",
              "      <td>0.310341</td>\n",
              "      <td>0.559701</td>\n",
              "      <td>0.572519</td>\n",
              "      <td>0.566038</td>\n",
              "      <td>0.939103</td>\n",
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          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
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          "data": {
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              "TrainOutput(global_step=440, training_loss=0.14870419022574258, metrics={'train_runtime': 4848.8784, 'train_samples_per_second': 0.726, 'train_steps_per_second': 0.091, 'total_flos': 189580172219712.0, 'train_loss': 0.14870419022574258, 'epoch': 20.0})"
            ]
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n"
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              "{'eval_loss': 0.3103410303592682,\n",
              " 'eval_precision': 0.5597014925373134,\n",
              " 'eval_recall': 0.5725190839694656,\n",
              " 'eval_f1': 0.5660377358490565,\n",
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              " 'eval_samples_per_second': 2.554,\n",
              " 'eval_steps_per_second': 0.348,\n",
              " 'epoch': 20.0}"
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          "execution_count": 33,
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      "source": [
        "trainer.evaluate()"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "lJjFpUvVr8gn",
        "outputId": "b36b10db-9b96-4a40-ccbf-ce05bc6fda9b"
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      "outputs": [
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: Coded, Sentences. If Coded, Sentences are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Prediction *****\n",
            "  Num examples = 44\n",
            "  Batch size = 8\n"
          ]
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              "{'CHI-SQUARE, FISHER EXACT': {'precision': 0.8,\n",
              "  'recall': 0.8,\n",
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              "  'number': 10},\n",
              " 'MIXED EFFECTS, MLMS, GROWTH MODELS, GENERALISED LMS (INC LOGREG)': {'precision': 0.4864864864864865,\n",
              "  'recall': 0.5625,\n",
              "  'f1': 0.5217391304347827,\n",
              "  'number': 32},\n",
              " 'NON-PARAMETRIC TESTS': {'precision': 0.17647058823529413,\n",
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              " 'OLS GLM': {'precision': 0.6615384615384615,\n",
              "  'recall': 0.5375,\n",
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              " 'SCALE DEVELOPMENT, FACTOR ANALYSIS, PCA': {'precision': 0.6,\n",
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          "execution_count": 34,
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        "predictions, labels, _ = trainer.predict(tokenized_datasets[\"test\"])\n",
        "predictions = np.argmax(predictions, axis=2)\n",
        "\n",
        "# Remove ignored index (special tokens)\n",
        "true_predictions = [\n",
        "    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "true_labels = [\n",
        "    [label_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
        "    for prediction, label in zip(predictions, labels)\n",
        "]\n",
        "\n",
        "results = metric.compute(predictions=true_predictions, references=true_labels, zero_division=1)\n",
        "results"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FKRxiybgTVWM"
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      "outputs": [],
      "source": [
        "model.save_pretrained(\"/content/drive/MyDrive/Colab Notebooks/scibert_cased-finetune\")"
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    {
      "cell_type": "markdown",
      "source": [
        "NER-BERT underperforming comparatively to the other models tested would imply the tokens we are trying to extract are non-standard, which is to be expected in the given context."
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