UniversalNER.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dtim-upc/THOR/blob/main/UniversalNER.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "import json\n",
        "from spacy import displacy\n",
        "from spacy.tokens import Span"
      ],
      "metadata": {
        "id": "dBG_OajqqT3T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m59.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.1/258.1 kB\u001b[0m \u001b[31m27.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m83.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m32.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m104.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m78.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q transformers einops accelerate langchain bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentencepiece\n",
        "!pip install xformers"
      ],
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        },
        "id": "oWnhhEUpqohc",
        "outputId": "922be7cd-d0b6-4af1-b0a3-37bf4a11a1f9"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.3 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/1.3 MB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n",
            "Collecting xformers\n",
            "  Downloading xformers-0.0.22-cp310-cp310-manylinux2014_x86_64.whl (211.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.6/211.6 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xformers) (1.23.5)\n",
            "Requirement already satisfied: torch==2.0.1 in /usr/local/lib/python3.10/dist-packages (from xformers) (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->xformers) (3.27.5)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->xformers) (17.0.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.0.1->xformers) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.0.1->xformers) (1.3.0)\n",
            "Installing collected packages: xformers\n",
            "Successfully installed xformers-0.0.22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -U 'spacy'"
      ],
      "metadata": {
        "id": "aYcJLmuECSxZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en_core_web_lg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJt3HAZ5CdXD",
        "outputId": "a06b56eb-fd88-4c67-831e-f1347238b5c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-10-04 18:43:47.699497: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Collecting en-core-web-lg==3.6.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.6.0/en_core_web_lg-3.6.0-py3-none-any.whl (587.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m587.7/587.7 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.7.0,>=3.6.0 in /usr/local/lib/python3.10/dist-packages (from en-core-web-lg==3.6.0) (3.6.1)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (8.1.12)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.0.10)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (0.9.0)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (0.10.2)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (4.66.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.23.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (1.10.13)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.1.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (23.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.0.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2023.7.22)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (0.1.3)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (8.1.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.7.0,>=3.6.0->en-core-web-lg==3.6.0) (2.1.3)\n",
            "Installing collected packages: en-core-web-lg\n",
            "Successfully installed en-core-web-lg-3.6.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_lg')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import en_core_web_lg\n",
        "nlp = en_core_web_lg.load()"
      ],
      "metadata": {
        "id": "SDXAJKXhCESx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# input file paths\n",
        "input_file_path1 = '/content/Masked_Text_Only_Test.json'\n",
        "input_text = []\n",
        "\n",
        "# open the JSON file containing a dictionary of {'text':'Given text...'}\n",
        "with open(input_file_path1, 'r', encoding='utf-8') as inp_txt_file:\n",
        "    for line in inp_txt_file:\n",
        "        # parse the lines as JSON objects\n",
        "        data = json.loads(line)\n",
        "        input_text.append(data['text'])\n",
        "\n",
        "print(len(input_text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TPrdZRu4l7bf",
        "outputId": "8bb1ea50-f607-45c1-df5f-f394d0ad7d37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "90\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import HuggingFacePipeline\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "model = \"Universal-NER/UniNER-7B-all\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model)\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\", #task\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"auto\",\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    max_length=3000,\n",
        "    do_sample=True,\n",
        "    top_k=1,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "9ij1fBlrqutt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
          "referenced_widgets": [
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    {
      "cell_type": "code",
      "source": [
        "temp = 0.7\n",
        "llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':temp})"
      ],
      "metadata": {
        "id": "yuFINVctq7ec"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import PromptTemplate,  LLMChain\n",
        "\n",
        "template = \"\"\"\n",
        "              A virtual assistant answers questions from a user based on the provided text.\n",
        "              USER: Text: {input_text}\n",
        "              ASSISTANT: I’ve read this text.\n",
        "              USER: What describes {entity_type} in the text?\n",
        "              ASSISTANT:\n",
        "           \"\"\"\n",
        "\n",
        "prompt = PromptTemplate(template=template, input_variables=[\"input_text\",\"entity_type\"])\n",
        "\n",
        "llm_chain = LLMChain(prompt=prompt, llm=llm)\n"
      ],
      "metadata": {
        "id": "rz80BItUrUKS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# text = \"??????? - Abdominal aortic aneurysm - BD50.4Z\\nAneurysms can develop anywhere along the aorta, but most aortic aneurysms occur in the part of the aorta that's in the belly area (abdomen). Several things can play a role in the development of an abdominal aortic aneurysm, including: Hardening of the arteries (atherosclerosis). Atherosclerosis occurs when fat and other substances build up on the lining of a blood vessel. High blood pressure. High blood pressure can damage and weaken the aorta's walls. Blood vessel diseases. These are diseases that cause blood vessels to become inflamed. Infection in the aorta. Rarely, a bacterial or fungal infection might cause an abdominal aortic aneurysms. Trauma. For example, being injured in a car accident can cause an abdominal aortic aneurysms. \"\n",
        "\n",
        "ENTITY_LABELS = ['Cause', 'Anatomy', 'Composition', 'Diagnosis', 'Surgery', 'Disease', 'Medicine', 'Precaution', 'Riskfactor', 'Symptom', 'Complication']\n",
        "\n",
        "entities_all = []\n",
        "entity_types_all = []\n",
        "\n",
        "\n",
        "for doc_no, text in enumerate(input_text):\n",
        "    pred_entities = []\n",
        "    pred_types = []\n",
        "    for entity_type in ENTITY_LABELS:\n",
        "        pred = llm_chain.run({\"input_text\":text,\"entity_type\":entity_type})\n",
        "        try:\n",
        "          pred = json.loads(pred)\n",
        "          pred_entities.extend(pred)\n",
        "          pred_types.extend([entity_type+'_E']*len(pred))\n",
        "        except:\n",
        "          print(f'JSON error in Doc = {doc_no}')\n",
        "\n",
        "    # pushing everything into a list of list - these will store all the predictions for the whole test set\n",
        "    entities_all.append(pred_entities)\n",
        "    entity_types_all.append(pred_types)\n"
      ],
      "metadata": {
        "id": "m4T-l0Yrr4JC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# showing a single output from the UniversalNER\n",
        "print(len(entities_all[0]) == len(entity_types_all[0]))\n",
        "print()\n",
        "print(f'entities = {entities_all[0]}')\n",
        "print(f'entity_types = {entity_types_all[0]}')\n",
        "print(len(entities_all))"
      ],
      "metadata": {
        "id": "l9ZzIlAVH9Vx",
        "outputId": "c7d0b588-f2c4-46d7-ea78-7290e4e85ee2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "\n",
            "entities = ['Hardening of the arteries', 'atherosclerosis', 'High blood pressure', 'Trauma', 'aorta', 'belly area', 'abdomen', 'Abdominal aortic aneurysm', 'Aneurysms', 'Hardening of the arteries', 'atherosclerosis', 'High blood pressure', 'Blood vessel diseases', 'Infection in the aorta', 'Trauma', 'High blood pressure', 'Hardening of the arteries', 'atherosclerosis', 'High blood pressure', 'damage', 'weaken', 'inflamed', 'Infection', 'injured', 'car accident']\n",
            "entity_types = ['Cause_E', 'Cause_E', 'Cause_E', 'Cause_E', 'Anatomy_E', 'Anatomy_E', 'Anatomy_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Riskfactor_E', 'Complication_E', 'Complication_E', 'Complication_E', 'Complication_E', 'Complication_E', 'Complication_E', 'Complication_E', 'Complication_E', 'Complication_E']\n",
            "90\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# text = \"??????? - Abdominal aortic aneurysm - BD50.4Z\\nAneurysms can develop anywhere along the aorta, but most aortic aneurysms occur in the part of the aorta that's in the belly area (abdomen). Several things can play a role in the development of an abdominal aortic aneurysm, including: Hardening of the arteries (atherosclerosis). Atherosclerosis occurs when fat and other substances build up on the lining of a blood vessel. High blood pressure. High blood pressure can damage and weaken the aorta's walls. Blood vessel diseases. These are diseases that cause blood vessels to become inflamed. Infection in the aorta. Rarely, a bacterial or fungal infection might cause an abdominal aortic aneurysms. Trauma. For example, being injured in a car accident can cause an abdominal aortic aneurysms. \"\n",
        "\n",
        "# entities = ['Hardening of the arteries', 'atherosclerosis', 'High blood pressure', 'Trauma', 'aorta', 'belly area', 'abdomen', 'blood vessel', \"aorta's walls\", 'Abdominal aortic aneurysm', 'Aneurysms', 'Hardening of the arteries', 'atherosclerosis', 'High blood pressure', 'Blood vessel diseases', 'Infection', 'Trauma', 'Hardening of the arteries', 'High blood pressure']\n",
        "# entity_types = ['Cause_E', 'Cause_E', 'Cause_E', 'Cause_E', 'Anatomy_E', 'Anatomy_E', 'Anatomy_E', 'Anatomy_E', 'Anatomy_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Disease_E', 'Riskfactor_E', 'Riskfactor_E']"
      ],
      "metadata": {
        "id": "-nl6IpHc-vgp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def find_entity_positions_UniNER(text, entities, entity_types):\n",
        "\n",
        "    # Initializing a dictionary to store the first occurrence of each entity type for each entity\n",
        "    entity_dict = {}\n",
        "    for entity, entity_type in zip(entities, entity_types):\n",
        "        if entity not in entity_dict:\n",
        "            entity_dict[entity] = entity_type\n",
        "\n",
        "    # List to store the entity indices along with their types\n",
        "    entity_indices = []\n",
        "    new_entity_types = []\n",
        "\n",
        "    for entity, entity_type in entity_dict.items():\n",
        "        start_index = 0\n",
        "        while True:\n",
        "            start_index = text.find(entity, start_index)\n",
        "            if start_index == -1:\n",
        "                break\n",
        "            end_index = start_index + len(entity)\n",
        "            entity_indices.append([start_index, end_index])\n",
        "            new_entity_types.append(entity_type)\n",
        "            start_index = end_index  # Move to the next position for the next search\n",
        "\n",
        "    # Displaying the result\n",
        "    # print(len(entity_indices))\n",
        "    # print(entity_indices)\n",
        "    return entity_indices, new_entity_types"
      ],
      "metadata": {
        "id": "Ax86EmFjqsW6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_token_span_from_char_span(doc, char_start, char_end):\n",
        "    start_token_index = None\n",
        "    end_token_index = None\n",
        "\n",
        "    for token in doc:\n",
        "        if start_token_index is None and token.idx >= char_start:\n",
        "            start_token_index = token.i\n",
        "        if token.idx + len(token) > char_end:\n",
        "            end_token_index = token.i\n",
        "            break\n",
        "\n",
        "    if start_token_index is not None and end_token_index is not None:\n",
        "        return (start_token_index, end_token_index)\n",
        "    else:\n",
        "        return None"
      ],
      "metadata": {
        "id": "FeG5-aLH9H9c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def construct_json(text, entity_positions, entity_types):\n",
        "    '''This function will construct the JSON file for evaluation and also generate span cat'''\n",
        "    entities_span = []\n",
        "    doc_span = []\n",
        "    orig_doc = nlp(text)\n",
        "\n",
        "    for ent_pos, ent_type in zip(entity_positions, entity_types):\n",
        "        # This will be used for SemEval Evaluation\n",
        "        entities_span.append((ent_pos[0], ent_pos[1], ent_type))\n",
        "\n",
        "        # This will be used to visualize the Span\n",
        "        token_span = get_token_span_from_char_span(orig_doc, ent_pos[0], ent_pos[1])\n",
        "        if token_span is not None:\n",
        "            doc_span.append(Span(orig_doc, token_span[0], token_span[1], ent_type))\n",
        "\n",
        "    # A dictionary in the format {'text': 'Tuberculosis generally damages the lungs', 'entities': [(0, 12, 'Disease_E'), (35, 40, 'Anatomy_E')]}\n",
        "    ner_prediction = {'text': text, 'entities': entities_span}\n",
        "    orig_doc.spans[\"sc\"] = doc_span\n",
        "\n",
        "    return orig_doc, ner_prediction"
      ],
      "metadata": {
        "id": "Ramq9sPJ_rtZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def save_predictions(ner_predictions, filename, semeval_format=True):\n",
        "  # Saving the predictions as JSON - each dictionary on a line\n",
        "  semeval_ent = []\n",
        "  with open(filename, 'w', encoding='UTF-8') as json_file:\n",
        "    for pred in ner_predictions:\n",
        "      if semeval_format:\n",
        "        # prodigy format to work with nereval library - for SemEval 2013 - 9.1 task.\n",
        "        tmp_ent = []\n",
        "        for ent in pred['entities']:\n",
        "          # saved in this format: [{\"label\": \"PER\", \"start\": 2, \"end\": 4}, ... ]\n",
        "          tmp_ent.append({\"label\": ent[2], \"start\": ent[0], \"end\": ent[1]})\n",
        "\n",
        "        semeval_ent.append(tmp_ent)\n",
        "\n",
        "      else:\n",
        "        # this is regullar spacy format, can be used for spacy's default evaluation later\n",
        "        json_file.write(json.dumps(pred, ensure_ascii=False))\n",
        "        json_file.write('\\n')\n",
        "\n",
        "    if semeval_format:\n",
        "      # dumping it into a JSON file\n",
        "      json_file.write(json.dumps(semeval_ent, ensure_ascii=False))\n",
        "\n",
        "  return semeval_ent\n",
        "  # # This is single line JSON Dump of the entile list of dictionary - parser cannot parse it directly\n",
        "  # with open(OUTPUT_DIR+'/predition.jsonl', 'w') as fout:\n",
        "  #     json.dump(ner_predictions, fout)"
      ],
      "metadata": {
        "id": "SxCOhKMJHj_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''Assigns different colors to the Entities during visualization.'''\n",
        "\n",
        "color_list = ['yellow', 'red', 'orange', '#0D9CB4', '#5813C7', '#0D350E', '#1AA436',\n",
        "          '#1AE0F9', '#BADCA1', '#78A2E5', '#D845FB', '#54B69E']\n",
        "\n",
        "colors = dict(zip(ENTITY_LABELS, color_list))\n",
        "options = {\"colors\": colors}"
      ],
      "metadata": {
        "id": "ycceoBo9-qEh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def render_pred_span_cat(doc):\n",
        "  '''Desired Format: doc.spans[\"sc\"] = [Span(doc, 3, 6, \"ORG\"), Span(doc, 5, 6, \"GPE\")]\n",
        "  '''\n",
        "  spacy.displacy.render(doc, style=\"span\", options=options, jupyter=True)"
      ],
      "metadata": {
        "id": "TBq5eS3G-eZi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ner_predictions_all = []\n",
        "for text, entities, entity_types in zip(input_text, entities_all, entity_types_all):\n",
        "    entity_positions, entity_types = find_entity_positions_UniNER(text, entities, entity_types)\n",
        "    doc, ner_pred_tmp = construct_json(text, entity_positions, entity_types)\n",
        "    ner_predictions_all.append(ner_pred_tmp)\n",
        "\n",
        "    _ = save_predictions(ner_predictions_all, filename=f'/content/UniNER_SemEval_Pred_{temp}.jsonl')\n",
        "    # Saving this for Experiment 2... Spacy Format\n",
        "    _ = save_predictions(ner_predictions_all, filename=f'/content/UniNER_Spacy_Pred_{temp}.jsonl', semeval_format=False)\n",
        "\n",
        "    # print(entity_positions)\n",
        "    # print(entity_types)\n",
        "    # print()\n",
        "\n",
        "    # render_pred_span_cat(doc)\n",
        "    # print()"
      ],
      "metadata": {
        "id": "qJipEMuD--Q5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download(f'/content/UniNER_SemEval_Pred_{temp}.jsonl')\n",
        "files.download(f'/content/UniNER_Spacy_Pred_{temp}.jsonl')"
      ],
      "metadata": {
        "id": "VSwiB48Xm6JY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "18be1fdc-9f76-4e72-a702-e14850bba91e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_fd3aea92-d7f0-47ea-aefa-2501a83e2fe8\", \"UniNER_SemEval_Pred_0.7.jsonl\", 63958)"
            ]
          },
          "metadata": {}
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_1b82f077-47da-4f8a-8775-20a1126a4bb1\", \"UniNER_Spacy_Pred_0.7.jsonl\", 140715)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}