03a-gmb-ner-xlmr.ipynb

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      "source": [
        "# Finetuning Named Entity Recognition using Trainer\n",
        "\n",
        "This is almost the same as the `03-gmb-ner-bert.ipynb` notebook except that we use the HuggingFace Trainer API to do the fine-tuning instead of native PyTorch.\n",
        "\n",
        "There are a number of advantages to using the Trainer API. First your code is less verbose and you automatically have access to best practices that are directly inbuilt into the Trainer API.\n",
        "\n",
        "The Trainer API works on datasets directly. You don't have to create a DataLoader explicitly.\n",
        "\n",
        "However, you do have to honor the expectations of the Trainer API. For example, the version of the `compute_metrics` function takes an `EvalPrediction` object rather than a pair of list of lists. So you need to learn a little more about the `Trainer` and `TrainerArguments` API but the benefits are quite good.\n",
        "\n",
        "In addition, we have switched out the BERT model for a larger and more powerful XLM-RoBERTa model, and as a result we see slightly better results from the trained model on the test split."
      ],
      "metadata": {
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    },
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      "cell_type": "markdown",
      "source": [
        "## Environment Setup"
      ],
      "metadata": {
        "id": "oAeunKAALALa"
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    },
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            "  Downloading frozenlist-1.3.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (144 kB)\n",
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            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=2f807cf17866004b228a36239f79b561330b4bb363f907fe2d7072585aa1de24\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
            "Successfully built seqeval\n",
            "Installing collected packages: multidict, frozenlist, yarl, urllib3, asynctest, async-timeout, aiosignal, pyyaml, fsspec, aiohttp, xxhash, tokenizers, sacremoses, responses, huggingface-hub, transformers, seqeval, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
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            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-2.0.0 frozenlist-1.3.0 fsspec-2022.3.0 huggingface-hub-0.5.1 multidict-6.0.2 pyyaml-6.0 responses-0.18.0 sacremoses-0.0.49 seqeval-1.2.2 tokenizers-0.11.6 transformers-4.18.0 urllib3-1.25.11 xxhash-3.0.0 yarl-1.7.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets seqeval"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "c0PpOtzvLKES"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import random\n",
        "import shutil\n",
        "import torch\n",
        "\n",
        "from collections import Counter, defaultdict\n",
        "from datasets import load_dataset, ClassLabel\n",
        "from seqeval.metrics import classification_report, f1_score\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
        "from torch.nn.functional import cross_entropy\n",
        "from torch.optim import AdamW\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import (\n",
        "    AutoTokenizer, AutoModelForTokenClassification, AutoConfig,\n",
        "    DataCollatorForTokenClassification,\n",
        "    TrainingArguments, Trainer, get_scheduler\n",
        ")"
      ],
      "metadata": {
        "id": "A1JMisQMLCNG"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Processing"
      ],
      "metadata": {
        "id": "QU85VlH7LQh3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hh-el_zLNiV",
        "outputId": "98446bc9-f248-48ea-9e1d-d10cb4b5852a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/odsc-2022-data/GMB/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6NM0-u1LWMC",
        "outputId": "8210e12c-c7ab-45e8-bbb5-1435fffa9194"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bert-base-cased-gmb-ner        ner.csv\t\txlm-roberta-base-gmb-ner\n",
            "distilbert-base-cased-gmb-ner  ner_dataset.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!head /content/drive/MyDrive/odsc-2022-data/GMB/ner_dataset.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3w91gc7Lbrx",
        "outputId": "df1b10e4-d134-4e7c-b704-3bdef6e2e844"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sentence #,Word,POS,Tag\r\n",
            "Sentence: 1,Thousands,NNS,O\r\n",
            ",of,IN,O\r\n",
            ",demonstrators,NNS,O\r\n",
            ",have,VBP,O\r\n",
            ",marched,VBN,O\r\n",
            ",through,IN,O\r\n",
            ",London,NNP,B-geo\r\n",
            ",to,TO,O\r\n",
            ",protest,VB,O\r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GS_DATA_DIR = \"/content/drive/MyDrive/odsc-2022-data/GMB/\"\n",
        "DATA_DIR = \"data\"\n",
        "\n",
        "NER_FILEPATH = os.path.join(GS_DATA_DIR, \"ner_dataset.csv\")\n",
        "\n",
        "OUTPUT_FILEPATHS = [\n",
        "  os.path.join(DATA_DIR, \"gmb-train.jsonl\"),\n",
        "  os.path.join(DATA_DIR, \"gmb-valid.jsonl\"),\n",
        "  os.path.join(DATA_DIR, \"gmb-test.jsonl\")\n",
        "]\n",
        "\n",
        "BASE_MODEL_NAME = \"xlm-roberta-base\"\n",
        "MODEL_DIR = os.path.join(DATA_DIR, \"{:s}-gmb-ner\".format(BASE_MODEL_NAME))"
      ],
      "metadata": {
        "id": "8jr9FsKbLerq"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def write_output(tokens, labels, output_files, num_writter):\n",
        "  assert(len(tokens) == len(labels))\n",
        "  rec = json.dumps({ \"tokens\": tokens, \"ner_tags\": labels })\n",
        "  dice = random.random()\n",
        "  if dice <= 0.7:\n",
        "    output_files[0].write(\"{:s}\\n\".format(rec))\n",
        "    num_written[0] += 1\n",
        "  elif dice <= 0.8:\n",
        "    output_files[1].write(\"{:s}\\n\".format(rec))\n",
        "    num_written[1] += 1\n",
        "  else:\n",
        "    output_files[2].write(\"{:s}\\n\".format(rec))\n",
        "    num_written[2] += 1\n",
        "\n",
        "\n",
        "os.makedirs(DATA_DIR, exist_ok=True)\n",
        "output_files = [open(filepath, \"w\") for filepath in OUTPUT_FILEPATHS]\n",
        "num_written = [0, 0, 0]\n",
        "tokens, labels = [], []\n",
        "with open(NER_FILEPATH, \"r\", encoding=\"latin-1\") as fner:\n",
        "  csv_reader = csv.reader(fner)\n",
        "  next(csv_reader)  # skip header\n",
        "  for row in csv_reader:\n",
        "    if row[0].startswith(\"Sentence\") and len(tokens) > 0:\n",
        "      # write out current sentence to train / valid / test\n",
        "      write_output(tokens, labels, output_files, num_written)\n",
        "      tokens, labels = [], []\n",
        "    # accumulate tokens and labels\n",
        "    tokens.append(row[1])\n",
        "    labels.append(row[3])\n",
        "    # if num_written[0] > 1000:\n",
        "    #   break\n",
        "\n",
        "if len(tokens) > 0:\n",
        "  write_output(tokens, labels, output_files, num_written)\n",
        "\n",
        "[output_file.close() for output_file in output_files]\n",
        "print(num_written)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcOpkOzLLsNK",
        "outputId": "fdcc0c4c-19a9-496b-a848-01c60fd3942e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[33575, 4814, 9570]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset"
      ],
      "metadata": {
        "id": "BXCoWvybL1WS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_files = {\n",
        "    \"train\": OUTPUT_FILEPATHS[0],\n",
        "    \"validation\": OUTPUT_FILEPATHS[1],\n",
        "    \"test\": OUTPUT_FILEPATHS[2]\n",
        "}\n",
        "gmb_dataset = load_dataset(\"json\", data_files=data_files)\n",
        "gmb_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436,
          "referenced_widgets": [
            "ae129b9a544f4388bd3a720c040e8e71",
            "02e5c7a3468d4a6089db89fc58d5b8fa",
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            "7b9060465f534da2a5e479b17f1149ce",
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            "5516850b7d594d8a916c1abd5c74b654",
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            "9ed7b9a0b5ea4193ada5490eeb4d527a",
            "d56f4c89d6714096b92dc667965d40a7",
            "66d44d435b3f4627817b2d3904bea614",
            "b25b5fb15bf14c9bb163eea61e211da6"
          ]
        },
        "id": "qLieIh90LzMN",
        "outputId": "5406762b-797e-4f2f-e6f3-3ec45c5614f7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-9aeaee0640bf6fd3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-9aeaee0640bf6fd3/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ae129b9a544f4388bd3a720c040e8e71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd2e07eccf864ba0b6d5160016b7d2c8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-9aeaee0640bf6fd3/0.0.0/ac0ca5f5289a6cf108e706efcf040422dbbfa8e658dee6a819f20d76bb84d26b. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d3c5fdd0dd842bf8e5a3aaf14fa0e31"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 33575\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 4814\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'ner_tags'],\n",
              "        num_rows: 9570\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenizer"
      ],
      "metadata": {
        "id": "Ch1KpfmgMIZQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Class Label Mappings"
      ],
      "metadata": {
        "id": "Z2BTUpozMd1g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "entity_types = set()\n",
        "for ner_tags in gmb_dataset[\"train\"][\"ner_tags\"]:\n",
        "  for ner_tag in ner_tags:\n",
        "    if ner_tag.startswith(\"B-\"):\n",
        "      entity_types.add(ner_tag.replace(\"B-\", \"\"))\n",
        "entity_types = sorted(list(entity_types))\n",
        "\n",
        "tag_names = []\n",
        "for entity_type in entity_types:\n",
        "  tag_names.append(\"B-{:s}\".format(entity_type))\n",
        "  tag_names.append(\"I-{:s}\".format(entity_type))\n",
        "tag_names.append(\"O\")\n",
        "\n",
        "tags = ClassLabel(names=tag_names)\n",
        "label2id = {name: tags.str2int(name) for name in tag_names}\n",
        "id2label = {id: tags.int2str(id) for id in range(len(tag_names))}\n",
        "\n",
        "# label2id, id2label"
      ],
      "metadata": {
        "id": "yD81Z93_MdOW"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Label Alignment"
      ],
      "metadata": {
        "id": "KwCi4XAXMbA5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_NAME)"
      ],
      "metadata": {
        "id": "Yv55CQvaMRM2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/8.68M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_and_align_labels(examples):\n",
        "  tokenized_inputs = tokenizer(examples[\"tokens\"], \n",
        "                               truncation=True, \n",
        "                               is_split_into_words=True)\n",
        "  aligned_batch_labels = []\n",
        "  for idx, labels in enumerate(examples[\"ner_tags\"]):\n",
        "    word_ids = tokenized_inputs.word_ids(batch_index=idx)\n",
        "    prev_word_id = None\n",
        "    aligned_labels = []\n",
        "    for word_id in word_ids:\n",
        "      if word_id is None or word_id == prev_word_id:\n",
        "        aligned_labels.append(-100)   # IGNore tag\n",
        "      else:\n",
        "        aligned_labels.append(label2id[labels[word_id]])\n",
        "      prev_word_id = word_id\n",
        "    aligned_batch_labels.append(aligned_labels)\n",
        "  tokenized_inputs[\"labels\"] = aligned_batch_labels\n",
        "  return tokenized_inputs\n",
        "\n",
        "\n",
        "tokens = gmb_dataset[\"train\"][0][\"tokens\"]\n",
        "ner_tags = gmb_dataset[\"train\"][0][\"ner_tags\"]\n",
        "aligned_labels = tokenize_and_align_labels(gmb_dataset[\"train\"][0:1])[\"labels\"][0]\n",
        "len(tokens), len(ner_tags), len(aligned_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hWf4UduL49J",
        "outputId": "47aeff1d-7655-49ad-e47d-95c4a1f5be1e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(24, 24, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_gmb_dataset = gmb_dataset.map(tokenize_and_align_labels,\n",
        "                                      batched=True,\n",
        "                                      remove_columns=[\"ner_tags\", \"tokens\"])\n",
        "encoded_gmb_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363,
          "referenced_widgets": [
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        "id": "NblcGcJHMPe5",
        "outputId": "aa72cb58-c8d3-4719-bfa1-d54a3064f204"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
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              "model_id": "58c5c83db53e40d8acdd3fb2b51acd40"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "368333d3b61c4ba5825f90b00553cf69"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/10 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "b735edf825f046fca8fe6d6efb4f6ddf"
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          "metadata": {}
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 33575\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 4814\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 9570\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model"
      ],
      "metadata": {
        "id": "b55t5_p4Mr4N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "-H75-MdlMogy"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForTokenClassification.from_pretrained(BASE_MODEL_NAME,\n",
        "                                                        num_labels=len(tag_names),\n",
        "                                                        id2label=id2label,\n",
        "                                                        label2id=label2id)\n",
        "model = model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158,
          "referenced_widgets": [
            "0a397cfff37f40d78d9da361c139c090",
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            "6e7aef2beb884655b9d49b7720587930",
            "ea20ca144b7f4e4fa23b1a0a0618a4f4"
          ]
        },
        "id": "BnCV9cGIMu_9",
        "outputId": "b76548bb-aea7-464e-b58b-a036f6dd327b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.04G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a397cfff37f40d78d9da361c139c090"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForTokenClassification: ['lm_head.layer_norm.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.bias']\n",
            "- This IS expected if you are initializing XLMRobertaForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing XLMRobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of XLMRobertaForTokenClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training\n",
        "\n",
        "Note different signatures for `compute_metrics` as opposed to compute_f1_score previously."
      ],
      "metadata": {
        "id": "ksGFgyCdM_9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 3\n",
        "batch_size = 16  # larger model, lower batch size\n",
        "logging_steps = len(encoded_gmb_dataset[\"train\"]) // batch_size\n",
        "model_name = \"{:s}-gmb-ner\".format(BASE_MODEL_NAME)\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=model_name,\n",
        "    log_level=\"error\",\n",
        "    num_train_epochs=num_epochs,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_steps=1e6,\n",
        "    weight_decay=0.01,\n",
        "    disable_tqdm=False,\n",
        "    logging_steps=logging_steps\n",
        ")"
      ],
      "metadata": {
        "id": "jH0wVGWqMx9b"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def align_predictions(labels_cpu, preds_cpu):\n",
        "  # remove -100 labels from score computation\n",
        "  batch_size, seq_len = preds_cpu.shape\n",
        "  labels_list, preds_list = [], []\n",
        "  for bid in range(batch_size):\n",
        "    example_labels, example_preds = [], []\n",
        "    for sid in range(seq_len):\n",
        "      # ignore label -100\n",
        "      if labels_cpu[bid, sid] != -100:\n",
        "        example_labels.append(id2label[labels_cpu[bid, sid]])\n",
        "        example_preds.append(id2label[preds_cpu[bid, sid]])\n",
        "    labels_list.append(example_labels)\n",
        "    preds_list.append(example_preds)\n",
        "  return labels_list, preds_list\n",
        "\n",
        "\n",
        "def compute_metrics(pred):\n",
        "  # convert logits to predictions and move to CPU\n",
        "  preds_cpu = np.argmax(pred.predictions, axis=-1)\n",
        "  labels_cpu = pred.label_ids\n",
        "  labels_list, preds_list = align_predictions(labels_cpu, preds_cpu)\n",
        "  # seqeval.metrics.f1_score takes list of list of tags\n",
        "  return { \"f1\": f1_score(labels_list, preds_list) }\n"
      ],
      "metadata": {
        "id": "QXjW-msxNDRS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorForTokenClassification(tokenizer)"
      ],
      "metadata": {
        "id": "c2AMc0LxOmUb"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config = AutoConfig.from_pretrained(BASE_MODEL_NAME, \n",
        "                                    num_labels=len(tag_names),\n",
        "                                    id2label=id2label, \n",
        "                                    label2id=label2id)\n",
        "def model_init():\n",
        "  return (AutoModelForTokenClassification\n",
        "          .from_pretrained(BASE_MODEL_NAME, config=config)\n",
        "          .to(device))"
      ],
      "metadata": {
        "id": "jHDbMyTpOrxp"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model_init=model_init,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=encoded_gmb_dataset[\"train\"],\n",
        "    eval_dataset=encoded_gmb_dataset[\"validation\"],\n",
        "    tokenizer=tokenizer\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "OZCaqCdMPNs4",
        "outputId": "047fa216-5adb-4e5b-f466-e09f8f5a5118"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='6297' max='6297' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [6297/6297 16:32, Epoch 3/3]\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>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.131700</td>\n",
              "      <td>0.098587</td>\n",
              "      <td>0.821738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.082300</td>\n",
              "      <td>0.088368</td>\n",
              "      <td>0.837120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.060400</td>\n",
              "      <td>0.087889</td>\n",
              "      <td>0.840959</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=6297, training_loss=0.09143616563652515, metrics={'train_runtime': 992.6859, 'train_samples_per_second': 101.467, 'train_steps_per_second': 6.343, 'total_flos': 2739354476419836.0, 'train_loss': 0.09143616563652515, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ],
      "metadata": {
        "id": "o2HTBQPtUTqv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_label_and_predicted_tags(examples):\n",
        "  features = [dict(zip(examples, t)) for t in zip(*examples.values())]\n",
        "  batch = data_collator(features)\n",
        "  input_ids = batch[\"input_ids\"].to(device)\n",
        "  attention_mask = batch[\"attention_mask\"].to(device)\n",
        "  labels = batch[\"labels\"].cpu().numpy()\n",
        "  with torch.no_grad():\n",
        "    output = trainer.model(input_ids, attention_mask=attention_mask)\n",
        "    predicted_label = torch.argmax(output.logits, dim=-1).cpu().numpy()\n",
        "  return { \n",
        "    \"labels\": labels,\n",
        "    \"predicted_labels\": predicted_label \n",
        "  }\n",
        "\n",
        "\n",
        "id2label[-100] = \"IGN\"\n",
        "test_labels_list, test_preds_list = [], []\n",
        "test_dataset = encoded_gmb_dataset[\"test\"].map(get_label_and_predicted_tags, batched=True)\n",
        "for test_rec in test_dataset:\n",
        "  lp_list = [(id2label[l], id2label[p]) \n",
        "             for l, p in zip(test_rec[\"labels\"], test_rec[\"predicted_labels\"])\n",
        "             if l != -100]\n",
        "  test_labels_list.append([l for l, p in lp_list])\n",
        "  test_preds_list.append([p for l, p in lp_list])\n",
        "\n",
        "print(classification_report(test_labels_list, test_preds_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355,
          "referenced_widgets": [
            "7e62d40b04c44f58a7b962b25c2b3ca2",
            "35a0b32c743341028a9cfd5816ee6428",
            "c04c89c3789d4546af45fce7becae9c1",
            "8851c6ca2f454e5fac3e72b1f29d5701",
            "6c35e8ad333d4b5fbc8485ad9bfdad53",
            "e771436624584068a25f2dd7f0fc69d1",
            "4476cc0147ba4653829853095cf0ef50",
            "ec7d8b08a8014eac9351af96e9cdc49c",
            "41a2f312ddfe410393071303040fcd5c",
            "a807b7576edb40b88d8697fcff7ef93b",
            "196291c0dc69449b9d9d4d027dfb9689"
          ]
        },
        "id": "685A8L1X5Hpg",
        "outputId": "7fcbc561-07c0-4f9f-cd41-c668b658d936"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Parameter 'function'=<function get_label_and_predicted_tags at 0x7f186c0739e0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e62d40b04c44f58a7b962b25c2b3ca2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         art       0.41      0.20      0.27        87\n",
            "         eve       0.36      0.37      0.36        57\n",
            "         geo       0.86      0.90      0.88      7435\n",
            "         gpe       0.96      0.95      0.95      3113\n",
            "         nat       0.55      0.53      0.54        49\n",
            "         org       0.75      0.72      0.73      4027\n",
            "         per       0.77      0.79      0.78      3331\n",
            "         tim       0.87      0.88      0.88      4059\n",
            "\n",
            "   micro avg       0.84      0.85      0.84     22158\n",
            "   macro avg       0.69      0.67      0.67     22158\n",
            "weighted avg       0.84      0.85      0.84     22158\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(ytrue, ypreds, labels):\n",
        "  cm = confusion_matrix(ytrue, ypreds, normalize=\"true\")\n",
        "  fig, ax = plt.subplots(figsize=(12, 12))\n",
        "  disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "  disp.plot(cmap=\"Blues\", values_format=\"0.2f\", ax=ax, colorbar=False)\n",
        "  plt.title(\"Normalized Confusion Matrix\")\n",
        "  _ = plt.show()\n",
        "\n",
        "\n",
        "flat_test_labels, flat_test_preds = [], []\n",
        "for test_labels, test_preds in zip(test_labels_list, test_preds_list):\n",
        "  flat_test_labels.extend(test_labels)\n",
        "  flat_test_preds.extend(test_preds)\n",
        "plot_confusion_matrix(flat_test_labels, flat_test_preds, tag_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "pcOFFlTtkSKA",
        "outputId": "f96e6a6b-0afe-446d-90a4-5867c7720cdf"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save Model"
      ],
      "metadata": {
        "id": "l8DFA8rjka1z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r xlm-roberta-base-gmb-ner/ /content/drive/MyDrive/odsc-2022-data/GMB/"
      ],
      "metadata": {
        "id": "KkK8v5bekcCt"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/drive/MyDrive/odsc-2022-data/GMB/xlm-roberta-base-gmb-ner/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4x2Upc5skeiX",
        "outputId": "e5339b4f-413b-4e87-8bae-023ef3a4f4dd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "runs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "pbflGC9y_hAv"
      },
      "execution_count": 23,
      "outputs": []
    }
  ]
}