Greek_NER_tests.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MarijkeBeersmans/CLS-project/blob/master/Greek_NER_tests.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's train some models baby"
      ],
      "metadata": {
        "id": "Kt7qlyqxH_b_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "TKa1OaeJaI0z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "978e8cd5-682f-407a-ed33-0b624888cd49"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.24.1-py3-none-any.whl (261 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.24.1\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
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            "Collecting Datasets\n",
            "  Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Downloading pyarrow_hotfix-0.5-py3-none-any.whl (7.8 kB)\n",
            "Collecting dill<0.3.8,>=0.3.0 (from Datasets)\n",
            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting multiprocess (from Datasets)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->Datasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->Datasets) (2.8.2)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->Datasets) (1.16.0)\n",
            "Installing collected packages: pyarrow-hotfix, dill, multiprocess, Datasets\n",
            "Successfully installed Datasets-2.15.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.5\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Cloning into 'glaux-nlp'...\n",
            "remote: Enumerating objects: 1964, done.\u001b[K\n",
            "remote: Counting objects: 100% (105/105), done.\u001b[K\n",
            "remote: Compressing objects: 100% (72/72), done.\u001b[K\n",
            "remote: Total 1964 (delta 40), reused 92 (delta 31), pack-reused 1859\u001b[K\n",
            "Receiving objects: 100% (1964/1964), 494.35 MiB | 30.76 MiB/s, done.\n",
            "Resolving deltas: 100% (388/388), done.\n",
            "Updating files: 100% (1011/1011), done.\n",
            "usage: git config [<options>]\n",
            "\n",
            "Config file location\n",
            "    --global              use global config file\n",
            "    --system              use system config file\n",
            "    --local               use repository config file\n",
            "    --worktree            use per-worktree config file\n",
            "    -f, --file <file>     use given config file\n",
            "    --blob <blob-id>      read config from given blob object\n",
            "\n",
            "Action\n",
            "    --get                 get value: name [value-pattern]\n",
            "    --get-all             get all values: key [value-pattern]\n",
            "    --get-regexp          get values for regexp: name-regex [value-pattern]\n",
            "    --get-urlmatch        get value specific for the URL: section[.var] URL\n",
            "    --replace-all         replace all matching variables: name value [value-pattern]\n",
            "    --add                 add a new variable: name value\n",
            "    --unset               remove a variable: name [value-pattern]\n",
            "    --unset-all           remove all matches: name [value-pattern]\n",
            "    --rename-section      rename section: old-name new-name\n",
            "    --remove-section      remove a section: name\n",
            "    -l, --list            list all\n",
            "    --fixed-value         use string equality when comparing values to 'value-pattern'\n",
            "    -e, --edit            open an editor\n",
            "    --get-color           find the color configured: slot [default]\n",
            "    --get-colorbool       find the color setting: slot [stdout-is-tty]\n",
            "\n",
            "Type\n",
            "    -t, --type <>         value is given this type\n",
            "    --bool                value is \"true\" or \"false\"\n",
            "    --int                 value is decimal number\n",
            "    --bool-or-int         value is --bool or --int\n",
            "    --bool-or-str         value is --bool or string\n",
            "    --path                value is a path (file or directory name)\n",
            "    --expiry-date         value is an expiry date\n",
            "\n",
            "Other\n",
            "    -z, --null            terminate values with NUL byte\n",
            "    --name-only           show variable names only\n",
            "    --includes            respect include directives on lookup\n",
            "    --show-origin         show origin of config (file, standard input, blob, command line)\n",
            "    --show-scope          show scope of config (worktree, local, global, system, command)\n",
            "    --default <value>     with --get, use default value when missing entry\n",
            "\n",
            "usage: git config [<options>]\n",
            "\n",
            "Config file location\n",
            "    --global              use global config file\n",
            "    --system              use system config file\n",
            "    --local               use repository config file\n",
            "    --worktree            use per-worktree config file\n",
            "    -f, --file <file>     use given config file\n",
            "    --blob <blob-id>      read config from given blob object\n",
            "\n",
            "Action\n",
            "    --get                 get value: name [value-pattern]\n",
            "    --get-all             get all values: key [value-pattern]\n",
            "    --get-regexp          get values for regexp: name-regex [value-pattern]\n",
            "    --get-urlmatch        get value specific for the URL: section[.var] URL\n",
            "    --replace-all         replace all matching variables: name value [value-pattern]\n",
            "    --add                 add a new variable: name value\n",
            "    --unset               remove a variable: name [value-pattern]\n",
            "    --unset-all           remove all matches: name [value-pattern]\n",
            "    --rename-section      rename section: old-name new-name\n",
            "    --remove-section      remove a section: name\n",
            "    -l, --list            list all\n",
            "    --fixed-value         use string equality when comparing values to 'value-pattern'\n",
            "    -e, --edit            open an editor\n",
            "    --get-color           find the color configured: slot [default]\n",
            "    --get-colorbool       find the color setting: slot [stdout-is-tty]\n",
            "\n",
            "Type\n",
            "    -t, --type <>         value is given this type\n",
            "    --bool                value is \"true\" or \"false\"\n",
            "    --int                 value is decimal number\n",
            "    --bool-or-int         value is --bool or --int\n",
            "    --bool-or-str         value is --bool or string\n",
            "    --path                value is a path (file or directory name)\n",
            "    --expiry-date         value is an expiry date\n",
            "\n",
            "Other\n",
            "    -z, --null            terminate values with NUL byte\n",
            "    --name-only           show variable names only\n",
            "    --includes            respect include directives on lookup\n",
            "    --show-origin         show origin of config (file, standard input, blob, command line)\n",
            "    --show-scope          show scope of config (worktree, local, global, system, command)\n",
            "    --default <value>     with --get, use default value when missing entry\n",
            "\n",
            "Cloning into 'greek_ner'...\n",
            "remote: Enumerating objects: 519, done.\u001b[K\n",
            "remote: Counting objects: 100% (392/392), done.\u001b[K\n",
            "remote: Compressing objects: 100% (187/187), done.\u001b[K\n",
            "remote: Total 519 (delta 239), reused 316 (delta 203), pack-reused 127\u001b[K\n",
            "Receiving objects: 100% (519/519), 109.94 MiB | 1.05 MiB/s, done.\n",
            "Resolving deltas: 100% (290/290), done.\n",
            "Updating files: 100% (221/221), done.\n"
          ]
        }
      ],
      "source": [
        "! pip install -U accelerate\n",
        "! pip install -U transformers\n",
        "! pip install Datasets\n",
        "! pip install torch\n",
        "# clone the glaux nlp repo\n",
        "\n",
        "!git clone https://github.com/alekkeersmaekers/glaux-nlp.git\n",
        "\n",
        "\n",
        "# clone my own greek_ner repo for the data and stuff\n",
        "\n",
        "!git config - global user.name \"u0161477\"\n",
        "!git config - global user.email \"marijke.beersmans@kuleuven.be\"\n",
        "!git clone https://Colab:glpat-W1ZtyMEnBVqYf6KHyzTW@gitlab.kuleuven.be/u0161477/greek_ner.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd greek_ner\n",
        "!git remote add origin https://Colab:glpat-W1ZtyMEnBVqYf6KHyzTW@gitlab.kuleuven.be/u0161477/greek_ner.git\n",
        "!git config - global user.name \"u0161477\"\n",
        "!git config --global user.email \"marijke.beersmans@kuleuven.be\"\n",
        "!git config --local -l\n",
        "!git add -A\n",
        "!git commit -m 'test'\n",
        "!git push"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4jyoanGXE3D",
        "outputId": "e5f91f8d-43c5-4390-cb91-7aa2f0f3c3ee"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/greek_ner\n",
            "error: remote origin already exists.\n",
            "usage: git config [<options>]\n",
            "\n",
            "Config file location\n",
            "    --global              use global config file\n",
            "    --system              use system config file\n",
            "    --local               use repository config file\n",
            "    --worktree            use per-worktree config file\n",
            "    -f, --file <file>     use given config file\n",
            "    --blob <blob-id>      read config from given blob object\n",
            "\n",
            "Action\n",
            "    --get                 get value: name [value-pattern]\n",
            "    --get-all             get all values: key [value-pattern]\n",
            "    --get-regexp          get values for regexp: name-regex [value-pattern]\n",
            "    --get-urlmatch        get value specific for the URL: section[.var] URL\n",
            "    --replace-all         replace all matching variables: name value [value-pattern]\n",
            "    --add                 add a new variable: name value\n",
            "    --unset               remove a variable: name [value-pattern]\n",
            "    --unset-all           remove all matches: name [value-pattern]\n",
            "    --rename-section      rename section: old-name new-name\n",
            "    --remove-section      remove a section: name\n",
            "    -l, --list            list all\n",
            "    --fixed-value         use string equality when comparing values to 'value-pattern'\n",
            "    -e, --edit            open an editor\n",
            "    --get-color           find the color configured: slot [default]\n",
            "    --get-colorbool       find the color setting: slot [stdout-is-tty]\n",
            "\n",
            "Type\n",
            "    -t, --type <>         value is given this type\n",
            "    --bool                value is \"true\" or \"false\"\n",
            "    --int                 value is decimal number\n",
            "    --bool-or-int         value is --bool or --int\n",
            "    --bool-or-str         value is --bool or string\n",
            "    --path                value is a path (file or directory name)\n",
            "    --expiry-date         value is an expiry date\n",
            "\n",
            "Other\n",
            "    -z, --null            terminate values with NUL byte\n",
            "    --name-only           show variable names only\n",
            "    --includes            respect include directives on lookup\n",
            "    --show-origin         show origin of config (file, standard input, blob, command line)\n",
            "    --show-scope          show scope of config (worktree, local, global, system, command)\n",
            "    --default <value>     with --get, use default value when missing entry\n",
            "\n",
            "core.repositoryformatversion=0\n",
            "core.filemode=true\n",
            "core.bare=false\n",
            "core.logallrefupdates=true\n",
            "remote.origin.url=https://Colab:glpat-W1ZtyMEnBVqYf6KHyzTW@gitlab.kuleuven.be/u0161477/greek_ner.git\n",
            "remote.origin.fetch=+refs/heads/*:refs/remotes/origin/*\n",
            "branch.main.remote=origin\n",
            "branch.main.merge=refs/heads/main\n",
            "[main b561c65] test\n",
            " 4 files changed, 122487 insertions(+)\n",
            " create mode 100644 experiments/models/config.json\n",
            " create mode 100644 experiments/models/model.safetensors\n",
            " create mode 100644 experiments/models/runs/Nov20_16-00-16_2355b3a9de99/events.out.tfevents.1700496022.2355b3a9de99.290.0\n",
            " create mode 100644 experiments/results/test.conll\n",
            "Enumerating objects: 13, done.\n",
            "Counting objects: 100% (13/13), done.\n",
            "Delta compression using up to 2 threads\n",
            "Compressing objects: 100% (10/10), done.\n",
            "Writing objects: 100% (11/11), 49.23 MiB | 701.00 KiB/s, done.\n",
            "Total 11 (delta 1), reused 0 (delta 0), pack-reused 0\n",
            "remote: \n",
            "remote: ========================================================================\u001b[K\n",
            "remote: \n",
            "remote:      The KU Leuven GitLab services will be down for maintenance on\u001b[K\n",
            "remote:      Thursday 23/11/2023 between 19:00 and 21:00. Follow status on\u001b[K\n",
            "remote: [https://status.kuleuven.io](https://status.kuleuven.io/posts/01hf4h9v9jz63h7r5qg9xmzxws).\u001b[K\n",
            "remote: \n",
            "remote: ========================================================================\u001b[K\n",
            "remote: \n",
            "To https://gitlab.kuleuven.be/u0161477/greek_ner.git\n",
            "   e2a746f..b561c65  main -> main\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "# # !pip install Datasets\n",
        "# !pip install transformers\n",
        "# # caution: path[0] is reserved for script path (or '' in REPL)\n",
        "sys.path.insert(1, '/content/glaux-nlp')\n",
        "\n",
        "train_path = '/content/greek_ner/experiments/final_dataset/conll/train.conll'\n",
        "\n",
        "test_path = '/content/greek_ner/experiments/final_dataset/conll/test.conll'\n",
        "\n",
        "val_path = '/content/greek_ner/experiments/final_dataset/conll/val.conll'\n",
        "\n",
        "from classification.Classifier import Classifier\n",
        "\n",
        "clrf = Classifier('mercelisw/electra-grc', '/content/greek_ner/experiments/models',\n",
        "                  tokenizer_path='mercelisw/electra-grc',\n",
        "                  training_data= train_path,\n",
        "                  test_data=test_path,\n",
        "                  ignore_label='O', data_preset='simple')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "d5dfba0f6150487cae6530b51ecdb9d5"
          ]
        },
        "id": "nYexQXi5RG_A",
        "outputId": "0d8280ce-2bcf-48c8-e69c-0b9e19954bf0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/383 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8963deb6e24487781a46351a3cb069f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/463k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cee65d4243624380a956db70330b10be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/709k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c4910798382f45cd8893a18a93abb6a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "fb99e8c1548b467c99479a41dc50392a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from data import Datasets\n",
        "from tokenization import Tokenization\n",
        "\n",
        "feature_cols = {'ID': 0,\n",
        "                                                      'FORM': 1,\n",
        "                                                      'MISC': 2}\n",
        "\n",
        "def read_tags(feature, data, in_feats=False, return_wids=True, return_tokens=True, return_tags=True):\n",
        "      # Reads the values for a given feature (UPOS, XPOS or morphological) from the training set, as well as the wids and forms\n",
        "      wid_sents = []\n",
        "      token_sents = []\n",
        "      tag_sents = []\n",
        "\n",
        "      for sent in data:\n",
        "          wids = []\n",
        "          tokens = []\n",
        "          tags = []\n",
        "          for line in sent.strip().split(\"\\n\"):\n",
        "              split = line.split(\"\\t\")\n",
        "              if return_wids:\n",
        "                  wids.append(split[feature_cols['ID']])\n",
        "              if return_tokens:\n",
        "                  tokens.append(split[feature_cols['FORM']])\n",
        "              if return_tags:\n",
        "                  if not in_feats:\n",
        "                      tags.append(split[feature_cols[feature]])\n",
        "\n",
        "          wid_sents.append(wids)\n",
        "          token_sents.append(tokens)\n",
        "          tag_sents.append(tags)\n",
        "      return token_sents, tag_sents\n",
        "\n",
        "tokens, tags = read_tags('MISC', clrf.training_data, in_feats=False,return_wids=False)\n",
        "tag_dict = {'MISC':tags}\n",
        "tokens_norm = Tokenization.normalize_tokens(tokens, 'greek_glaux')\n",
        "tag2id, id2tag = clrf.id_label_mappings(tags)\n",
        "training_data = Datasets.build_dataset(tokens_norm,tag_dict)\n",
        "training_data = training_data.map(Tokenization.tokenize_sentence,fn_kwargs={\"tokenizer\":clrf.tokenizer})\n",
        "training_data = training_data.map(clrf.align_labels,fn_kwargs={\"prefix_subword_id\":clrf.prefix_subword_id,\"tag2id\":tag2id})\n",
        "clrf.train_classifier(clrf.model_dir,training_data,tag2id=tag2id,id2tag=id2tag,epochs=3,batch_size=16,learning_rate=5e-5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651,
          "referenced_widgets": [
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        },
        "id": "ac0loVaXWG0y",
        "outputId": "b1219d67-368b-45e1-c45c-9bc3fe7dba83"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/33421 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ef5600424db492a98c67efec0b6e536"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/33421 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "984e10966a564ac186c2e8c531f91ea3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/364 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59a0ce3af01d4f87962ee6a533044dc3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/58.0M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2286ba06a37548b1ad7f59d8eb0b09f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of ElectraForTokenClassification were not initialized from the model checkpoint at mercelisw/electra-grc and are newly initialized: ['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",
            "You're using a ElectraTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6267' max='6267' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6267/6267 05:04, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.005400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.725200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.640900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.604900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.529100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.504200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.492000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.489700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.441600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.425800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.423800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.422200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  clrf = clrf(args.transformer_path,args.model_dir,args.tokenizer_path,args.training_data,args.test_data,args.ignore_label,args.unknown_label,args.data_preset,feature_cols)\n",
        "\n",
        "def read_tags(feature, data, in_feats=False, return_wids=True, return_tokens=True, return_tags=True):\n",
        "      # Reads the values for a given feature (UPOS, XPOS or morphological) from the training set, as well as the wids and forms\n",
        "      wid_sents = []\n",
        "      token_sents = []\n",
        "      tag_sents = []\n",
        "\n",
        "      for sent in data:\n",
        "          wids = []\n",
        "          tokens = []\n",
        "          tags = []\n",
        "          for line in sent.strip().split(\"\\n\"):\n",
        "              split = line.split(\"\\t\")\n",
        "              if return_wids:\n",
        "                  wids.append(split[feature_cols['ID']])\n",
        "              if return_tokens:\n",
        "                  tokens.append(split[feature_cols['FORM']])\n",
        "              if return_tags:\n",
        "                  if not in_feats:\n",
        "                      tags.append(split[feature_cols[feature]])\n",
        "\n",
        "          wid_sents.append(wids)\n",
        "          token_sents.append(tokens)\n",
        "          tag_sents.append(tags)\n",
        "      return wid_sents, token_sents, tag_sents\n",
        "\n",
        "\n",
        "\n",
        "wids, tokens, tags = read_tags('MISC', clrf.test_data, False)\n",
        "tags_dict = {'MISC':tags}\n",
        "tokens_norm = tokens\n",
        "tokens_norm = Tokenization.normalize_tokens(tokens, 'greek_glaux')\n",
        "test_data = Datasets.build_dataset(tokens,tags_dict)\n",
        "test_data = test_data.map(Tokenization.tokenize_sentence,fn_kwargs={\"tokenizer\":clrf.tokenizer})\n",
        "prediction = clrf.predict(test_data,model_dir=clrf.model_dir,batch_size=16)\n",
        "clrf.write_prediction(wids,tokens,tags,prediction,'/content/greek_ner/experiments/results/test.conll','CONLL')"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install accelerate -U\n",
        "\n",
        "import re\n",
        "from pathlib import Path\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "tag2id, id2tag = test.id_label_mappings([['O', 'MISC', 'I-PERS', 'B-PERS', 'I-LOC', 'B-LOC', 'I-GRP', 'B-GRP']])\n",
        "\n",
        "# print(tag2id)\n",
        "# print(id2tag)\n",
        "\n",
        "from data.CONLLReader import CONLLReader\n",
        "\n",
        "feature_cols = {'ID': 0,\n",
        "                                                      'FORM': 1,\n",
        "                                                      'MISC': 2}\n",
        "\n",
        "reader = CONLLReader(preset='simple', feature_cols=feature_cols)\n",
        "\n",
        "train_data = reader.parse_conll(train_path)\n",
        "val_data = reader.parse_conll(val_path)\n",
        "\n",
        "# train_wids, train_tokens, train_tags = reader.read_tags('MISC', train_data)\n",
        "\n",
        "# val_wids, val_tokens, val_tags = reader.read_tags('MISC', val_data)\n",
        "\n",
        "# sents = train_data[0:10]\n",
        "\n",
        "# print(reader.feature_cols['FORM'])\n",
        "\n",
        "\n",
        "def read_tags(feature, data, in_feats=False, return_wids=True, return_tokens=True, return_tags=True):\n",
        "      # Reads the values for a given feature (UPOS, XPOS or morphological) from the training set, as well as the wids and forms\n",
        "      wid_sents = []\n",
        "      token_sents = []\n",
        "      tag_sents = []\n",
        "\n",
        "      for sent in data:\n",
        "          wids = []\n",
        "          tokens = []\n",
        "          tags = []\n",
        "          for line in sent.strip().split(\"\\n\"):\n",
        "              split = line.split(\"\\t\")\n",
        "              if return_wids:\n",
        "                  wids.append(split[feature_cols['ID']])\n",
        "              if return_tokens:\n",
        "                  tokens.append(split[feature_cols['FORM']])\n",
        "              if return_tags:\n",
        "                  if not in_feats:\n",
        "                      tags.append(split[feature_cols[feature]])\n",
        "\n",
        "          wid_sents.append(wids)\n",
        "          token_sents.append(tokens)\n",
        "          tag_sents.append(tags)\n",
        "      return wid_sents, token_sents, tag_sents\n",
        "      # print('DONE!')\n",
        "\n",
        "\n",
        "# test2 = read_tags('MISC', val_data)\n",
        "# test2[10]\n",
        "\n",
        "train_wids, train_tokens, train_tags = read_tags('MISC', train_data)\n",
        "\n",
        "val_wids, val_tokens, val_tags = read_tags('MISC', val_data)\n",
        "\n",
        "# train_encodings = clrf.tokenizer(train_tokens, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)\n",
        "# val_encodings = clrf.tokenizer(val_tokens, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)\n",
        "\n",
        "# class NERDataset(torch.utils.data.Dataset):\n",
        "#     def __init__(self, encodings, labels):\n",
        "#         self.encodings = encodings\n",
        "#         self.labels = labels\n",
        "\n",
        "#     def __getitem__(self, idx):\n",
        "#         item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "#         item['labels'] = torch.tensor(self.labels[idx])\n",
        "#         return item\n",
        "\n",
        "#     def __len__(self):\n",
        "#         return len(self.labels)\n",
        "\n",
        "# train_labels = clrf.align_labels()\n",
        "# val_labels = clrf.align_labels()\n",
        "import tokenization\n",
        "print(train_tokens[1])\n",
        "tokens_norm = Tokenization.normalize_tokens([train_tokens[1]], normalization_rule='greek_glaux')\n",
        "print(tokens_norm)\n",
        "clrf.tokenizer.tokenize(tokens_norm[0], is_split_into_words=True)\n",
        "\n",
        "# train_encodings.pop(\"offset_mapping\") # we don't want to pass this to the model\n",
        "# val_encodings.pop(\"offset_mapping\")\n",
        "# train_dataset = NERDataset(train_encodings, train_labels)\n",
        "# val_dataset = NERDataset(val_encodings, val_labels)\n",
        "\n",
        "\n",
        "# train_labels = test.align_labels(val_tags)\n",
        "# val_labels =\n",
        "\n",
        "\n",
        "# test.train_classifier(output_model='electra_ner_test', train_dataset, tag2id=tag2id, id2tag=id2tag)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwL3DlaXgm2M",
        "outputId": "05a81b0e-4be1-49e0-aec6-e2d22f47461e"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Ἀβραὰμ', 'ἐγέννησεν', 'τὸν', 'Ἰσαάκ', ',', 'Ἰσαὰκ', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Ἰακώβ', ',', 'Ἰακὼβ', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Ἰούδαν', 'καὶ', 'τοὺς', 'ἀδελφοὺς', 'αὐτοῦ', ',', 'Ἰούδας', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Φάρες', 'καὶ', 'τὸν', 'Ζάρα', 'ἐκ', 'τῆς', 'Θαμάρ', ',', 'Φάρες', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Ἑσρώμ', ',', 'Ἑσρὼμ', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Ἀράμ', ',', 'Ἀρὰμ', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Ἀμιναδάβ', ',', 'Ἀμιναδὰβ', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Ναασσών', ',', 'Ναασσὼν', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Σαλμών', ',', 'Σαλμὼν', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Βόες', 'ἐκ', 'τῆς', 'Ῥαχάβ', ',', 'Βόες', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Ἰωβὴδ', 'ἐκ', 'τῆς', 'Ῥούθ', ',', 'Ἰωβὴδ', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Ἰεσσαί', ',', 'Ἰεσσαὶ', 'δὲ', 'ἐγέννησεν', 'τὸν', 'Δαυεὶδ', 'τὸν', 'βασιλέα']\n",
            "[['Ἀβραάμ', 'ἐγέννησεν', 'τόν', 'Ἰσαάκ', ',', 'Ἰσαάκ', 'δέ', 'ἐγέννησεν', 'τόν', 'Ἰακώβ', ',', 'Ἰακώβ', 'δέ', 'ἐγέννησεν', 'τόν', 'Ἰούδαν', 'καί', 'τούς', 'ἀδελφούς', 'αὐτοῦ', ',', 'Ἰούδας', 'δέ', 'ἐγέννησεν', 'τόν', 'Φάρες', 'καί', 'τόν', 'Ζάρα', 'ἐκ', 'τῆς', 'Θαμάρ', ',', 'Φάρες', 'δέ', 'ἐγέννησεν', 'τόν', 'Ἑσρώμ', ',', 'Ἑσρώμ', 'δέ', 'ἐγέννησεν', 'τόν', 'Ἀράμ', ',', 'Ἀράμ', 'δέ', 'ἐγέννησεν', 'τόν', 'Ἀμιναδάβ', ',', 'Ἀμιναδάβ', 'δέ', 'ἐγέννησεν', 'τόν', 'Ναασσών', ',', 'Ναασσών', 'δέ', 'ἐγέννησεν', 'τόν', 'Σαλμών', ',', 'Σαλμών', 'δέ', 'ἐγέννησεν', 'τόν', 'Βόες', 'ἐκ', 'τῆς', 'Ῥαχάβ', ',', 'Βόες', 'δέ', 'ἐγέννησεν', 'τόν', 'Ἰωβήδ', 'ἐκ', 'τῆς', 'Ῥούθ', ',', 'Ἰωβήδ', 'δέ', 'ἐγέννησεν', 'τόν', 'Ἰεσσαί', ',', 'Ἰεσσαί', 'δέ', 'ἐγέννησεν', 'τόν', 'Δαυείδ', 'τόν', 'βασιλέα']]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Ἀβραάμ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Ἰσαάκ',\n",
              " ',',\n",
              " 'Ἰσαάκ',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Ἰακώβ',\n",
              " ',',\n",
              " 'Ἰακώβ',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Ἰούδαν',\n",
              " 'καί',\n",
              " 'τούς',\n",
              " 'ἀδελφούς',\n",
              " 'αὐτοῦ',\n",
              " ',',\n",
              " 'Ἰούδας',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Φά',\n",
              " '##ρες',\n",
              " 'καί',\n",
              " 'τόν',\n",
              " 'Ζα',\n",
              " '##́',\n",
              " '##ρα',\n",
              " 'ἐκ',\n",
              " 'τῆς',\n",
              " 'Θα',\n",
              " '##μάρ',\n",
              " ',',\n",
              " 'Φά',\n",
              " '##ρες',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Ἑ',\n",
              " '##σ',\n",
              " '##ρώ',\n",
              " '##μ',\n",
              " ',',\n",
              " 'Ἑ',\n",
              " '##σ',\n",
              " '##ρώ',\n",
              " '##μ',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Ἀρά',\n",
              " '##μ',\n",
              " ',',\n",
              " 'Ἀρά',\n",
              " '##μ',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Ἀμ',\n",
              " '##ινα',\n",
              " '##δά',\n",
              " '##β',\n",
              " ',',\n",
              " 'Ἀμ',\n",
              " '##ινα',\n",
              " '##δά',\n",
              " '##β',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Να',\n",
              " '##ασ',\n",
              " '##σώ',\n",
              " '##ν',\n",
              " ',',\n",
              " 'Να',\n",
              " '##ασ',\n",
              " '##σώ',\n",
              " '##ν',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Σαλ',\n",
              " '##μών',\n",
              " ',',\n",
              " 'Σαλ',\n",
              " '##μών',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Βό',\n",
              " '##ες',\n",
              " 'ἐκ',\n",
              " 'τῆς',\n",
              " 'Ῥαχ',\n",
              " '##άβ',\n",
              " ',',\n",
              " 'Βό',\n",
              " '##ες',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Ἰω',\n",
              " '##βή',\n",
              " '##δ',\n",
              " 'ἐκ',\n",
              " 'τῆς',\n",
              " 'Ῥου',\n",
              " '##́',\n",
              " '##θ',\n",
              " ',',\n",
              " 'Ἰω',\n",
              " '##βή',\n",
              " '##δ',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Ἰε',\n",
              " '##σσαι',\n",
              " '##́',\n",
              " ',',\n",
              " 'Ἰε',\n",
              " '##σσαι',\n",
              " '##́',\n",
              " 'δέ',\n",
              " 'ἐγέννησεν',\n",
              " 'τόν',\n",
              " 'Δαυείδ',\n",
              " 'τόν',\n",
              " 'βασιλέα']"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    }
  ]
}