bert_finetuned_bpmn.ipynb

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            "\u001b[K     |████████████████████████████████| 5.8 MB 8.5 MB/s \n",
            "\u001b[?25hCollecting datasets\n",
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            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
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            "Collecting xxhash\n",
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            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
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            "Collecting sentencepiece!=0.1.92,>=0.1.91\n",
            "  Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 68.9 MB/s \n",
            "\u001b[?25hInstalling collected packages: urllib3, xxhash, tokenizers, responses, multiprocess, huggingface-hub, transformers, sentencepiece, datasets, evaluate\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",
            "Successfully installed datasets-2.7.1 evaluate-0.3.0 huggingface-hub-0.11.1 multiprocess-0.70.14 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.25.1 urllib3-1.25.11 xxhash-3.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading our .jsonl file into a Hugging Face dataset format\n",
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"json\", data_files=\"annotated_data.jsonl\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
            "8554733dceea41a3be7ae25259ab1157",
            "46a8bcd137e149719660d9eaf99f9ad8",
            "a87550c8894247b9a9bf39f96128b457",
            "963abd61b5ff4ce7a72a598301b7b094",
            "7224962b408a41f4826014bb9e8a2d96",
            "7a8c299db9c34657917284afa7e629a3",
            "e26081610c55435ea39869d7f8233aed",
            "6397b11e823849a39d970d0a944fec9c",
            "5a2d06ab2aeb48be9de9a3a70004ec64",
            "0b4b1d7e46c848719ebec5acf535723a",
            "86a7149f5a0d4def9ab64422441f63db",
            "25dbfed46a66464d8b524acf2e524f76",
            "8799b353a20945df90ba9d1ce2d87a2d",
            "54c6db6a2a624be0a1b7a25526424743",
            "a83a2944ba1c41dcbaf312d130e88e37",
            "716c7e5b46754e368cdffd153023113b",
            "377c83b7ef7542d88ea497d526e7dfa9",
            "3ce5bd53c49949bb8110de1f138a8669",
            "b8ad30e3e41340e0a1a496e60d8b4d1d",
            "1a543d631aef42868df8bb25b9b0ffa0",
            "d66b387c866048e5a6687bd1e9e60565",
            "f24ff35b73954eaba610723ae1981982",
            "7c74d6ecff144f0aa47c75e59bf9d0bc",
            "851c6ca1444a42f5bac8abfd79ed8d05",
            "a9d1af9290cf4084be4b300b00dc91f9",
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            "5c869a8706f6479ca5247fc94983bf43",
            "8fa7707267d040c1bf8783851f634778",
            "1f62fe45fbd84382ac2d032eb9247630",
            "048b6c62ed6642e3b68c43ad05b94645",
            "c7dd4fd3a239444ba1b1414079d86ca9",
            "b9253142d65b423188366669bd79f3c9"
          ]
        },
        "id": "in_8c3UTLSZC",
        "outputId": "185f4678-127c-494a-d5e4-8dc3ea0de544"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-40ab6dc6acfde962\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-40ab6dc6acfde962/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8554733dceea41a3be7ae25259ab1157"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25dbfed46a66464d8b524acf2e524f76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7c74d6ecff144f0aa47c75e59bf9d0bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-40ab6dc6acfde962/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0e14f826d814c1fa96a934c6471cfc7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Seeing what our dataset currently looks like\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gni1fl-fLdss",
        "outputId": "a5611215-1cf5-4c9c-eeff-17900e6e8b6b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', '_input_hash', '_task_hash', 'spans', 'meta', 'tokens', '_view_id', 'answer', '_timestamp'],\n",
              "        num_rows: 82\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing unnecessary columns\n",
        "dataset = dataset.remove_columns([\"text\", \"_input_hash\", \"_task_hash\", \"meta\", \"_view_id\", \"answer\", \"_timestamp\"])"
      ],
      "metadata": {
        "id": "DAcTZMpzMH8l"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Our dataset now looks like this\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ds0I6HJhMNgW",
        "outputId": "af7e27d7-1abf-458a-9a47-d4686ce723bd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['spans', 'tokens'],\n",
              "        num_rows: 82\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Example span:\", dataset[\"train\"][0][\"spans\"][0])\n",
        "print(\"Example token:\", dataset[\"train\"][0][\"tokens\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVuihj1GMQ2b",
        "outputId": "481b01b7-2eba-4e5c-a30f-9361151ed4ed"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example span: {'start': 24, 'end': 44, 'token_start': 4, 'token_end': 6, 'label': 'AGENT', 'text': None, 'pattern': None}\n",
            "Example token: {'text': 'The', 'start': 0, 'end': 3, 'id': 0, 'ws': True}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The thing we currently call \"dataset\" is actually a DatasetDict object\n",
        "actual_dataset = dataset[\"train\"]\n",
        "actual_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y12wOAD9Mkjo",
        "outputId": "d9b00d8a-cf22-4a40-83e1-b11940f20329"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['spans', 'tokens'],\n",
              "    num_rows: 82\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create train and test split\n",
        "new_dataset_dict = actual_dataset.train_test_split(test_size=0.1)"
      ],
      "metadata": {
        "id": "i6lB6EA2Mtiq"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_dataset_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gAbLBXw5NJii",
        "outputId": "07b26c04-1d27-4542-f5ee-1516b65142b0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['spans', 'tokens'],\n",
              "        num_rows: 73\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['spans', 'tokens'],\n",
              "        num_rows: 9\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data preprocessing"
      ],
      "metadata": {
        "id": "UioVUOogOPmx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "id2label = {\n",
        "    0: \"O\",\n",
        "    1: \"B-AGENT\",\n",
        "    2: \"I-AGENT\",\n",
        "    3: \"B-TASK\",\n",
        "    4: \"I-TASK\"\n",
        "}\n",
        "\n",
        "label2id = {\n",
        "    \"O\": 0,\n",
        "    \"B-AGENT\": 1,\n",
        "    \"I-AGENT\": 2,\n",
        "    \"B-TASK\": 3,\n",
        "    \"I-TASK\": 4\n",
        "}"
      ],
      "metadata": {
        "id": "aPY-RUu6OS9j"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_labels_and_tokens_to_dataset (example):\n",
        "\n",
        "  # Create the \"labels\" list\n",
        "  labels = []\n",
        "\n",
        "  for token in example[\"tokens\"]:\n",
        "    # Set a flag to indicate whether the token is within a span\n",
        "    within_span = False\n",
        "    for span in example[\"spans\"]:\n",
        "      # Check if the current token is within the span\n",
        "      if span[\"token_start\"] <= token[\"id\"] <= span[\"token_end\"]:\n",
        "        # Set the flag to True because the token is within a span\n",
        "        within_span = True\n",
        "        if span[\"token_start\"] == token[\"id\"]:\n",
        "          # The token is the beginning of the span, so it should have the \"B-\" prefix\n",
        "            labels.append(1 if span[\"label\"] == \"AGENT\" else 3)\n",
        "            break\n",
        "        else:\n",
        "          # The token is within the span, but not at the beginning, so it should have the \"I-\" prefix\n",
        "          labels.append(2 if span[\"label\"] == \"AGENT\" else 4)\n",
        "          break\n",
        "    # If the token is not within any of the spans, append the default 0 value\n",
        "    if not within_span:\n",
        "      labels.append(0)\n",
        "\n",
        "  example[\"labels\"] = labels\n",
        "  example[\"tokens\"] = [token[\"text\"] for token in example[\"tokens\"]]\n",
        "\n",
        "  return example"
      ],
      "metadata": {
        "id": "3v7DcaVYOXdj"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "updated_dataset_dict = new_dataset_dict.map(add_labels_and_tokens_to_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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          ]
        },
        "id": "ZvTZuX2XOeT2",
        "outputId": "9ff2fa0c-60b1-49f9-84a3-4b2cb64fc286"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/73 [00:00<?, ?ex/s]"
            ],
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            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "updated_dataset_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7BYYI7rOk8s",
        "outputId": "2e49df6d-53ea-47fd-c2c5-a6bb62b99da8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['spans', 'tokens', 'labels'],\n",
              "        num_rows: 73\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['spans', 'tokens', 'labels'],\n",
              "        num_rows: 9\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We don't need the spans column anymore\n",
        "updated_dataset_dict = updated_dataset_dict.remove_columns(\"spans\")"
      ],
      "metadata": {
        "id": "Bdi6fnzbOmK5"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "updated_dataset_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_E_1dkAOqY6",
        "outputId": "8317f700-52ab-4e0b-fde8-7aee1b08bcac"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['tokens', 'labels'],\n",
              "        num_rows: 73\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['tokens', 'labels'],\n",
              "        num_rows: 9\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Example tokens:\", updated_dataset_dict[\"train\"][0][\"tokens\"][:13])\n",
        "print(\"Example labels:\", updated_dataset_dict[\"train\"][0][\"labels\"][:13])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6fccgc_OrWa",
        "outputId": "777c3290-1d2e-42d0-b2b1-640bbb4403a2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Example tokens: ['The', 'process', 'starts', 'when', 'a', 'job', 'seeker', 'begins', 'their', 'search', 'for', 'a', 'job']\n",
            "Example labels: [0, 0, 0, 0, 1, 2, 2, 3, 4, 4, 4, 4, 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_checkpoint = \"bert-base-cased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "4da8436657fd41cabd17e384f5e4223b",
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            "38b42108242d435887b66c72eaeb4b06",
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            "3e291a9b0243453b9010be25ac44da4e",
            "2c3696f9f9004531a29e96ff67c152c3",
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            "69d581d8254f4268b9be581041f59888",
            "15f44857f248467a810c8fff21312e9b",
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        },
        "id": "xsGNNrntPDhP",
        "outputId": "338b64b0-7097-4ceb-c5d9-3d151e6f931f"
      },
      "execution_count": 17,
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        {
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              "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
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        {
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        {
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenization example\n",
        "inputs = tokenizer(updated_dataset_dict[\"train\"][4][\"tokens\"], is_split_into_words=True)"
      ],
      "metadata": {
        "id": "cEj_TkXXPuIx"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(inputs.tokens()[:10])\n",
        "print(inputs.tokens()[60:70])\n",
        "print(inputs.tokens()[120:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cr0gkvLQNO8",
        "outputId": "3de27fc2-132c-4541-a55d-d334260b1d72"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['[CLS]', 'The', 'process', 'starts', 'when', 'an', 'individual', 'applies', 'for', 'a']\n",
            "['government', 'department', 'will', 'issue', 'a', 'work', 'permit', '.', 'The', 'individual']\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inputs.word_ids()[:10])\n",
        "print(inputs.word_ids()[60:70])\n",
        "print(inputs.word_ids()[120:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAZHNI-uRNCO",
        "outputId": "083ed6c1-1178-4e14-b9e5-9875c3dd04cf"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[None, 0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
            "[59, 60, 61, 62, 63, 64, 65, 66, 67, 68]\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# After tokenization, our labels don't align with the tokens anymore\n",
        "print(\"Labels lenght:\", len(updated_dataset_dict[\"train\"][4][\"labels\"]))\n",
        "print(\"Tokens lenght before tokenization:\", len(updated_dataset_dict[\"train\"][4][\"tokens\"]))\n",
        "print(\"Tokens lenght after tokenization:\", len(inputs.tokens()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1cZDcqLRr1Y",
        "outputId": "a132ef7a-8a72-4397-cfa9-b6f8b593c70c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Labels lenght: 118\n",
            "Tokens lenght before tokenization: 118\n",
            "Tokens lenght after tokenization: 120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def align_labels_with_tokens(labels, word_ids):\n",
        "    new_labels = []\n",
        "    current_word = None\n",
        "    for word_id in word_ids:\n",
        "        if word_id != current_word:\n",
        "            # Start of a new word\n",
        "            current_word = word_id\n",
        "            label = -100 if word_id is None else labels[word_id]\n",
        "            new_labels.append(label)\n",
        "        elif word_id is None:\n",
        "            # Special token\n",
        "            new_labels.append(-100)\n",
        "        else:\n",
        "            # Same word as previous token\n",
        "            label = labels[word_id]\n",
        "            # If the label is B-XXX we change it to I-XXX\n",
        "            if label % 2 == 1:\n",
        "                label += 1\n",
        "            new_labels.append(label)\n",
        "\n",
        "    return new_labels"
      ],
      "metadata": {
        "id": "yHUt-rT9Rbde"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = updated_dataset_dict[\"train\"][4][\"labels\"]\n",
        "word_ids = inputs.word_ids()\n",
        "print(\"Old labels (first 10):\", labels[:10])\n",
        "print(\"New labels (first 10):\", align_labels_with_tokens(labels, word_ids)[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y__xdYKfSro7",
        "outputId": "b02ebb54-afe0-4b51-d60a-3d2eab229e71"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old labels (first 10): [0, 0, 0, 0, 1, 2, 3, 4, 4, 4]\n",
            "New labels (first 10): [-100, 0, 0, 0, 0, 1, 2, 3, 4, 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Old labels length:\", len(labels))\n",
        "print(\"New labels length:\", len(align_labels_with_tokens(labels, word_ids)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0froQxqTU4q",
        "outputId": "e6cfd5ea-4d15-4e90-b63f-bacea14d6dab"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Old labels length: 118\n",
            "New labels length: 120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Our tokens and our labels are now aligned\n",
        "example = inputs.tokens()[53:66]\n",
        "old_labels = list(map(lambda x: id2label[x], labels[53:66]))\n",
        "new_labels = list(map(lambda x: id2label[x], align_labels_with_tokens(labels, word_ids)[53:66])) \n",
        "print(\"Tokenized example:\", example)\n",
        "print(\"Old, unaligned labels:\", old_labels)\n",
        "print(\"New, aligned labels:\", new_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vRpWMm2T3qs",
        "outputId": "eb0a41eb-aeae-4e5e-ea49-cb7e5bf374d6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenized example: ['individual', 'does', 'meet', 'the', 'criteria', ',', 'the', 'government', 'department', 'will', 'issue', 'a', 'work']\n",
            "Old, unaligned labels: ['O', 'O', 'O', 'O', 'O', 'B-AGENT', 'I-AGENT', 'I-AGENT', 'O', 'B-TASK', 'I-TASK', 'I-TASK', 'I-TASK']\n",
            "New, aligned labels: ['O', 'O', 'O', 'O', 'O', 'O', 'B-AGENT', 'I-AGENT', 'I-AGENT', 'O', 'B-TASK', 'I-TASK', 'I-TASK']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A function to tokenize the whole dataset and align the labels\n",
        "def tokenize_and_align_labels(examples):\n",
        "    tokenized_inputs = tokenizer(\n",
        "        examples[\"tokens\"], truncation=True, is_split_into_words=True\n",
        "    )\n",
        "    all_labels = examples[\"labels\"]\n",
        "    new_labels = []\n",
        "    for i, labels in enumerate(all_labels):\n",
        "        word_ids = tokenized_inputs.word_ids(i)\n",
        "        new_labels.append(align_labels_with_tokens(labels, word_ids))\n",
        "\n",
        "    tokenized_inputs[\"labels\"] = new_labels\n",
        "    return tokenized_inputs"
      ],
      "metadata": {
        "id": "fYxLr8UKV-TI"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize the whole dataset\n",
        "tokenized_datasets = updated_dataset_dict.map(\n",
        "    tokenize_and_align_labels,\n",
        "    batched=True,\n",
        "    remove_columns=updated_dataset_dict[\"train\"].column_names\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "9a44e0125d534d16a02b8fdc74dbe81a",
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            "a781f967f6cf4b83a91019519a6aeae1"
          ]
        },
        "id": "X99D9Li6XSmw",
        "outputId": "16aa42bd-4ff9-43a9-e376-b344df9148a4"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?ba/s]"
            ],
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          "metadata": {}
        },
        {
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              "  0%|          | 0/1 [00:00<?, ?ba/s]"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aeimLW1WX9-8",
        "outputId": "932d1873-618d-4941-d465-c053930ec6dc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 73\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 9\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Labels:\", tokenized_datasets[\"train\"][0][\"labels\"][:10])\n",
        "print(\"Input IDs:\", tokenized_datasets[\"train\"][0][\"input_ids\"][:10])\n",
        "print(\"Token type IDs:\", tokenized_datasets[\"train\"][0][\"token_type_ids\"][:10])\n",
        "print(\"Attention mask:\", tokenized_datasets[\"train\"][0][\"attention_mask\"][:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLHhryCiYZm5",
        "outputId": "caa42ece-221c-4a3f-f7f2-7e6bb4fb93bb"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Labels: [-100, 0, 0, 0, 0, 1, 2, 2, 2, 3]\n",
            "Input IDs: [101, 1109, 1965, 3816, 1165, 170, 2261, 5622, 1200, 3471]\n",
            "Token type IDs: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "Attention mask: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Labels length:\", len(tokenized_datasets[\"train\"][0][\"labels\"]))\n",
        "print(\"Input IDs length:\", len(tokenized_datasets[\"train\"][0][\"input_ids\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lvd-mMewXyDt",
        "outputId": "1601993c-3af0-468e-be45-3c11f4657ade"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Labels length: 192\n",
            "Input IDs length: 192\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The samples have various lengths\n",
        "print(len(tokenized_datasets[\"train\"][0][\"labels\"]))\n",
        "print(len(tokenized_datasets[\"train\"][1][\"labels\"]))\n",
        "print(len(tokenized_datasets[\"train\"][2][\"labels\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UdUoTCLYubk",
        "outputId": "426099aa-9f80-4e86-c442-3fe46ef65a91"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "192\n",
            "64\n",
            "115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot histogram of sample lengths\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "lengths = []\n",
        "\n",
        "for sample in tokenized_datasets[\"train\"][\"labels\"]:\n",
        "  lengths.append(len(sample))\n",
        "\n",
        "plt.hist(lengths, bins=15)\n",
        "plt.title('Sample lengths')\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "1MZcuqYoeVbY",
        "outputId": "1621f47d-0470-4651-b287-fd160d9168ed"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shortest sample length:\", min(lengths))\n",
        "print(\"Longest sample length:\", max(lengths))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4QcBnbJfkuM",
        "outputId": "b56ac78a-847e-4805-c7da-a95d0540b103"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shortest sample length: 46\n",
            "Longest sample length: 251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have to pad the tokens and the labels so that they have the same length."
      ],
      "metadata": {
        "id": "dQwVCyFugvrV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForTokenClassification\n",
        "\n",
        "data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "PyZb_EJ-gCVU"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77om0niRhcCf",
        "outputId": "7a201300-f982-4d19-f333-42c434653bc9"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 73\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['labels', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 9\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is an example of how the DataCollator would pad the samples if we passed in the whole dataset:"
      ],
      "metadata": {
        "id": "1SuNArVhiaKz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For demonstration purposes, we pass in the whole \"train\" dataset\n",
        "batch = data_collator(tokenized_datasets[\"train\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eV5gFQCZg0Mm",
        "outputId": "58447287-463b-4d49-a452-b18b857ea518"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BertTokenizerFast 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_lengths = []\n",
        "\n",
        "for sample in batch[\"labels\"]:\n",
        "  new_lengths.append(len(sample))\n",
        "\n",
        "plt.hist(new_lengths, bins=15)\n",
        "plt.title('New sample lengths')\n",
        "plt.xlabel('Value')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "oHFmkn2riOrR",
        "outputId": "5a9ab8e7-15ba-4c7c-fba1-0cd3de29494e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shortest sample length (labels):\", min(new_lengths))\n",
        "print(\"Longest sample length (labels):\", max(new_lengths))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ki2Jrxk3in8X",
        "outputId": "95467a18-52f1-4362-edf9-63777d2dea18"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shortest sample length (labels): 251\n",
            "Longest sample length (labels): 251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, all sample labels get padded to the length of the longest sample.\n",
        "\n",
        "So do the input IDs:"
      ],
      "metadata": {
        "id": "R3ZV5dcRi4iU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_ids_lengths = []\n",
        "\n",
        "for sample in batch[\"input_ids\"]:\n",
        "  input_ids_lengths.append(len(sample))\n",
        "\n",
        "print(\"Shortest sample length (input_ids):\", min(input_ids_lengths))\n",
        "print(\"Longest sample length (input_ids):\", max(input_ids_lengths))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7N1c-2Sji3is",
        "outputId": "832822d3-42c9-4b06-e86f-a2e2e17dfac8"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shortest sample length (input_ids): 251\n",
            "Longest sample length (input_ids): 251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The input IDs get padded with 0s\n",
        "batch[\"input_ids\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ny1v0k_ojiJ7",
        "outputId": "b8c72b68-b0e9-4e16-de4e-3462587c44e2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([  101,  1109,  1965,  3816,  1165,   170,  2261,  5622,  1200,  3471,\n",
              "         1147,  3403,  1111,   170,  2261,   119,  1109,  2261,  5622,  1200,\n",
              "         1169,  1173,  1844,  3209,  2261,  6305,  1105,  2561,   170,  2190,\n",
              "         1104,  3209, 13673,   119,  1109,  2261,  5622,  1200,  1169,  1173,\n",
              "         2519,  1149,  1106,  1103, 13673,  1113,  1103,  2190,  1106,  1107,\n",
              "        15174,  1164,  1501,  3638,   119,  1409,  1126,  1501,  1700,  5903,\n",
              "          117,  1103,  2261,  5622,  1200,  1169, 12295,  1147, 14926,  1105,\n",
              "         2267,  2998,   119,  1109, 11440,  1169,  1173,  3189,  1103, 14926,\n",
              "         1105,  2267,  2998,  1105,  4958,  2480,  1137,  1136,  1106, 13967,\n",
              "         1103,  2261,  5622,  1200,  1111,  1126,  3669,   119,  1409,  1103,\n",
              "         2383,  1110,  1106, 13967,   117,  1103,  2261,  5622,  1200,  1169,\n",
              "         1173,  7034,  1111,  1103,  3669,  1105,  4739,  1103,  3669,   119,\n",
              "         1258,  1103,  3669,   117,  1103, 11440,  1169,  4958,  1106, 16589,\n",
              "         1137,  4392,  1103,  2261,  5622,  1200,   112,   188,  4048,   119,\n",
              "         1409,  1103,  4048,  1110,  5164,   117,  1103,  1965,  5374,  1110,\n",
              "         1845,   119,  1409,  1103,  4048,  1110,  3134,   117,  1103,  2261,\n",
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              "         1173,  3295,  1147,  1207,  2261,  1105,  3146, 24438,  1965,  5374,\n",
              "          119,   102,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
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              "            0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
              "            0])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The labels get padded with -100\n",
        "batch[\"labels\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHaocoIhjyZx",
        "outputId": "a1a0c79b-b442-4f8d-d8b0-bb8b2e9d44ef"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([-100,    0,    0,    0,    0,    1,    2,    2,    2,    3,    4,    4,\n",
              "           4,    4,    4,    0,    1,    2,    2,    2,    0,    0,    3,    4,\n",
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              "           4,    0,    0,    0,    0,    0,    0,    0,    0,    1,    2,    2,\n",
              "           2,    0,    0,    3,    4,    4,    4,    0,    3,    4,    4,    0,\n",
              "           0,    0,    0,    0,    1,    2,    0,    3,    4,    4,    4,    4,\n",
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              "           0,    0,    0,    0,    0,    0,    0,    1,    2,    2,    2,    0,\n",
              "           0,    3,    4,    4,    4,    0,    0,    0,    0,    0,    0, -100,\n",
              "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
              "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
              "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
              "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
              "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tuning"
      ],
      "metadata": {
        "id": "1SomU119luW_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install library to evaluate token classification predictions\n",
        "!pip install seqeval"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPxYKUezki2O",
        "outputId": "b24114cf-7a21-444b-d39f-ef4f7bc79c15"
      },
      "execution_count": 42,
      "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 seqeval\n",
            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
            "\u001b[K     |████████████████████████████████| 43 kB 1.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.8/dist-packages (from seqeval) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.8/dist-packages (from seqeval) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.2.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.7.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\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=16179 sha256=59504beb20558ec13267949febdcd1e3564dc455d0832f5d67e4006b94c6fac8\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/5c/ba/05fa33fa5855777b7d686e843ec07452f22a66a138e290e732\n",
            "Successfully built seqeval\n",
            "Installing collected packages: seqeval\n",
            "Successfully installed seqeval-1.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"seqeval\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "903cc6f9dc6744828f0bb2b6911fcba3",
            "451536cc18c8490aa1ca0b5e4763703e",
            "5683368dd2f442f780526e8d7cb7351b",
            "0f7c86f8a36548a989f361c28b4c3a55",
            "bcde03dabbe947e4a4f027fe8ebc63e6",
            "215fd2039bce4930aec3d272c7829298",
            "bc095a99a05e42c8b442c203e74c87c0",
            "52bd0da46f804fd38e656948373ca0e9",
            "dbf54dd76b5c4c9eb3f3d8354ff69085",
            "10bc27fa6eae4fa48cd774c8c7aade4f",
            "8c84728dc12d422db2de7b6269a143f7"
          ]
        },
        "id": "1EqYwg-Xlv1R",
        "outputId": "88946b78-6c09-4e66-eb5f-5f678b20d2d6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "903cc6f9dc6744828f0bb2b6911fcba3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using seqeval with Hugging Face Evaluate: https://huggingface.co/spaces/evaluate-metric/seqeval"
      ],
      "metadata": {
        "id": "sBL35VwYo8Dq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# seqeval demo\n",
        "correct_labels = list(map(lambda x: id2label[x], updated_dataset_dict[\"train\"][0][\"labels\"][:13]))\n",
        "fake_preds = correct_labels.copy()\n",
        "fake_preds[-4] = \"O\"\n",
        "\n",
        "print(\"Correct labels:\", correct_labels)\n",
        "print(\"Fake predictions:\", fake_preds)\n",
        "print(\"\\n\")\n",
        "metric.compute(predictions=[fake_preds], references=[correct_labels])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVfXyBOcnZ0C",
        "outputId": "73b46a77-3936-4f71-b297-bd677bc90c38"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correct labels: ['O', 'O', 'O', 'O', 'B-AGENT', 'I-AGENT', 'I-AGENT', 'B-TASK', 'I-TASK', 'I-TASK', 'I-TASK', 'I-TASK', 'I-TASK']\n",
            "Fake predictions: ['O', 'O', 'O', 'O', 'B-AGENT', 'I-AGENT', 'I-AGENT', 'B-TASK', 'I-TASK', 'O', 'I-TASK', 'I-TASK', 'I-TASK']\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'AGENT': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1},\n",
              " 'TASK': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1},\n",
              " 'overall_precision': 0.3333333333333333,\n",
              " 'overall_recall': 0.5,\n",
              " 'overall_f1': 0.4,\n",
              " 'overall_accuracy': 0.9230769230769231}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining an evaluation function\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    \n",
        "    logits, labels = eval_preds\n",
        "\n",
        "    # Extract index of logit with maximum value (i.e. the predicted labels)\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "\n",
        "    # Convert labels and predictions from integers to strings and remove special tokens (-100)\n",
        "    true_labels = [[id2label[l] for l in label if l != -100] for label in labels]\n",
        "    true_predictions = [\n",
        "        [id2label[p] for (p, l) in zip(prediction, label) if l != -100]\n",
        "        for prediction, label in zip(predictions, labels)\n",
        "    ]\n",
        "\n",
        "    all_metrics = metric.compute(predictions=true_predictions, references=true_labels)\n",
        "\n",
        "    return {\n",
        "        \"precision\": all_metrics[\"overall_precision\"],\n",
        "        \"recall\": all_metrics[\"overall_recall\"],\n",
        "        \"f1\": all_metrics[\"overall_f1\"],\n",
        "        \"accuracy\": all_metrics[\"overall_accuracy\"],\n",
        "    }"
      ],
      "metadata": {
        "id": "vEKGJHj4l0VM"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForTokenClassification\n",
        "\n",
        "model = AutoModelForTokenClassification.from_pretrained(\n",
        "    model_checkpoint,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "ffb0e41120674838a850afe27578d4f2",
            "b8720e685720409b84871063e07ba446",
            "e5d2a0bbdac04bfdbb1d41e1fa86a7fd",
            "86821a9ea3d649eb9df03e30bdcd35a8",
            "c0afadf232e04e27b0e43712fa501915",
            "51b823cb66294cdfaa842f55d30fa382",
            "5035f57d49c34fea92736ef1e1765114",
            "571ce77781814f1c9d7b5e037f47a402",
            "597e5896f9b141a1a1939a47aae05ead",
            "4b5fcba2043449699b140da97178b6e3",
            "818b3c557ce74c0cb9a2735d9a336fca"
          ]
        },
        "id": "_qbh4VuUtvzn",
        "outputId": "47120131-9132-404a-aca9-2041265ce43d"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffb0e41120674838a850afe27578d4f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForTokenClassification: ['cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification 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 BertForTokenClassification were not initialized from the model checkpoint at bert-base-cased 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.num_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMMrWRsmuJCq",
        "outputId": "5e09f95e-1085-4f17-8166-a97cadf20d86"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "feb5bfb3090d41689a3e33c99e3760a3",
            "1a9c12162df6439291f9ca3c87bf7688",
            "6ec86b31284f4dd6ba332c39dd77f291",
            "a1390181bc2b4b08acfd326d7b276dbc",
            "d7a6a00ab8f84e7cb1337457f7744a82",
            "e12d770bfa3d4074adf6edec7d982a6f",
            "28e9c4defeca42eca390e6d6901a1c4f",
            "e4244a40bfd0432c9e63daa88845a009",
            "34014c45668e4448bdd7cdcbf6f4a56e",
            "e10bf655e5f74600b60b1c7b4faf6125",
            "c14f54c5bf254af98d3d0e0b30c37f3e",
            "44f2c376536142a9bf0d3711a58ff731",
            "1548d107191041f384dc91774a084e44",
            "af5137b7be4f4e2d9b939092bdf1821c",
            "01d7e97ee00442c0b6330efb0e8b670a",
            "15147344c1e6439b92aeea1dd472f44e",
            "27dc10a3d7f5480a85f5af4bd6cf5943"
          ]
        },
        "id": "DIsTj3w1wvRj",
        "outputId": "78dcc064-0ea7-43e7-c445-ed325814a75b"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining our training arguments\n",
        "\n",
        "from transformers import TrainingArguments\n",
        "\n",
        "args = TrainingArguments(\n",
        "    \"bert-finetuned-bpmn\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    num_train_epochs=5,\n",
        "    weight_decay=0.01,\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "DVnXi4huw2lr"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=tokenized_datasets[\"train\"],\n",
        "    eval_dataset=tokenized_datasets[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        "    tokenizer=tokenizer,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pYf4bRSoxFn-",
        "outputId": "73bbb00e-de8e-4f8d-d7a1-72b2ec6dc8c2"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/huggingface_hub/repository.py:725: FutureWarning: Creating a repository through 'clone_from' is deprecated and will be removed in v0.12. Please create the repository first using `create_repo(..., exists_ok=True)`.\n",
            "  warnings.warn(\n",
            "Cloning https://huggingface.co/jtlicardo/bert-finetuned-bpmn into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/jtlicardo/bert-finetuned-bpmn into local empty directory.\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: 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",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 73\n",
            "  Num Epochs = 5\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 = 50\n",
            "  Number of trainable parameters = 107723525\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='50' max='50' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [50/50 00:45, Epoch 5/5]\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>No log</td>\n",
              "      <td>0.843653</td>\n",
              "      <td>0.189922</td>\n",
              "      <td>0.320261</td>\n",
              "      <td>0.238443</td>\n",
              "      <td>0.700540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.496653</td>\n",
              "      <td>0.542056</td>\n",
              "      <td>0.758170</td>\n",
              "      <td>0.632153</td>\n",
              "      <td>0.841727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.340300</td>\n",
              "      <td>0.671875</td>\n",
              "      <td>0.843137</td>\n",
              "      <td>0.747826</td>\n",
              "      <td>0.886691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.282114</td>\n",
              "      <td>0.692308</td>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.752239</td>\n",
              "      <td>0.890288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.265599</td>\n",
              "      <td>0.731429</td>\n",
              "      <td>0.836601</td>\n",
              "      <td>0.780488</td>\n",
              "      <td>0.893885</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 9\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to bert-finetuned-bpmn/checkpoint-10\n",
            "Configuration saved in bert-finetuned-bpmn/checkpoint-10/config.json\n",
            "Model weights saved in bert-finetuned-bpmn/checkpoint-10/pytorch_model.bin\n",
            "tokenizer config file saved in bert-finetuned-bpmn/checkpoint-10/tokenizer_config.json\n",
            "Special tokens file saved in bert-finetuned-bpmn/checkpoint-10/special_tokens_map.json\n",
            "tokenizer config file saved in bert-finetuned-bpmn/tokenizer_config.json\n",
            "Special tokens file saved in bert-finetuned-bpmn/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 9\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to bert-finetuned-bpmn/checkpoint-20\n",
            "Configuration saved in bert-finetuned-bpmn/checkpoint-20/config.json\n",
            "Model weights saved in bert-finetuned-bpmn/checkpoint-20/pytorch_model.bin\n",
            "tokenizer config file saved in bert-finetuned-bpmn/checkpoint-20/tokenizer_config.json\n",
            "Special tokens file saved in bert-finetuned-bpmn/checkpoint-20/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 9\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to bert-finetuned-bpmn/checkpoint-30\n",
            "Configuration saved in bert-finetuned-bpmn/checkpoint-30/config.json\n",
            "Model weights saved in bert-finetuned-bpmn/checkpoint-30/pytorch_model.bin\n",
            "tokenizer config file saved in bert-finetuned-bpmn/checkpoint-30/tokenizer_config.json\n",
            "Special tokens file saved in bert-finetuned-bpmn/checkpoint-30/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 9\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to bert-finetuned-bpmn/checkpoint-40\n",
            "Configuration saved in bert-finetuned-bpmn/checkpoint-40/config.json\n",
            "Model weights saved in bert-finetuned-bpmn/checkpoint-40/pytorch_model.bin\n",
            "tokenizer config file saved in bert-finetuned-bpmn/checkpoint-40/tokenizer_config.json\n",
            "Special tokens file saved in bert-finetuned-bpmn/checkpoint-40/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 9\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to bert-finetuned-bpmn/checkpoint-50\n",
            "Configuration saved in bert-finetuned-bpmn/checkpoint-50/config.json\n",
            "Model weights saved in bert-finetuned-bpmn/checkpoint-50/pytorch_model.bin\n",
            "tokenizer config file saved in bert-finetuned-bpmn/checkpoint-50/tokenizer_config.json\n",
            "Special tokens file saved in bert-finetuned-bpmn/checkpoint-50/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=50, training_loss=0.6322269439697266, metrics={'train_runtime': 49.1484, 'train_samples_per_second': 7.426, 'train_steps_per_second': 1.017, 'total_flos': 33379524614280.0, 'train_loss': 0.6322269439697266, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588,
          "referenced_widgets": [
            "9085a0cfe2ff47cda558d0bbc44b5d73",
            "772691f5cc734337a21ce2b277195aa7",
            "9bbac9baabab4e3bba3eee2c0cb82324",
            "e721a6337e0e4b26beab4d55bfb7011b",
            "d7d2d2ec7c9747cca39f3ec1672070d8",
            "a7d69cb0c59d4533b9db2c2ecef5a5fb",
            "63b240462b5945f78d02b493994c1797",
            "6385b125305a45718d4f8911246a8ea4",
            "2762d70b760649ffa7b7357a58f85687",
            "2890ff2f985f4e14a778256bb7f89c24",
            "7842f8c86c1341f9b9c5967f38f0de1a",
            "5c685fe359294db39e6379015c38fecd",
            "4560807e03bf426fb382e6e77facd1c1",
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            "7b95eb478eed4e3c8c62705d5079ccad",
            "b13bcadd319f48579d214e221466893f",
            "677571ed97864a988ef68d99c38db8b0",
            "5e045df8f2484c4cb5609044ddb14422",
            "9d178842832f49d4bcc5c1aa228a802b",
            "79c78f787dba43f4884875851f2e558b",
            "19c522ac8ad243a8ac413f1db6c03764",
            "e2bf07a27678467ba9dd061e6afbd970"
          ]
        },
        "id": "swHbboknyxOS",
        "outputId": "a4a43597-c70c-4eb1-aa17-a827c113450f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to bert-finetuned-bpmn\n",
            "Configuration saved in bert-finetuned-bpmn/config.json\n",
            "Model weights saved in bert-finetuned-bpmn/pytorch_model.bin\n",
            "tokenizer config file saved in bert-finetuned-bpmn/tokenizer_config.json\n",
            "Special tokens file saved in bert-finetuned-bpmn/special_tokens_map.json\n",
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.30k/411M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9085a0cfe2ff47cda558d0bbc44b5d73"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Dec10_16-02-31_0dfb9e6ee3eb/events.out.tfevents.1670688513.0dfb9e6ee3eb.76.0:  51%|#####1    …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5c685fe359294db39e6379015c38fecd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "remote: Scanning LFS files for validity, may be slow...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/jtlicardo/bert-finetuned-bpmn\n",
            "   b2947ba..745e658  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:remote: Scanning LFS files for validity, may be slow...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/jtlicardo/bert-finetuned-bpmn\n",
            "   b2947ba..745e658  main -> main\n",
            "\n",
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Token Classification', 'type': 'token-classification'}, 'metrics': [{'name': 'Precision', 'type': 'precision', 'value': 0.7314285714285714}, {'name': 'Recall', 'type': 'recall', 'value': 0.8366013071895425}, {'name': 'F1', 'type': 'f1', 'value': 0.7804878048780487}, {'name': 'Accuracy', 'type': 'accuracy', 'value': 0.8938848920863309}]}\n",
            "To https://huggingface.co/jtlicardo/bert-finetuned-bpmn\n",
            "   745e658..f38dd3f  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/jtlicardo/bert-finetuned-bpmn\n",
            "   745e658..f38dd3f  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/jtlicardo/bert-finetuned-bpmn/commit/745e658bb2ddff3bfbc0d76cd2fea9debee6c1e9'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    }
  ]
}