BERT and BART-Dataset-1.ipynb

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        "8c2a817e6b1c419cbe26722e96421ab9": {
          "model_module": "@jupyter-widgets/controls",
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          "model_module_version": "1.5.0",
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    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "medalpaca/medical_meadow_cord19  **DATASET** using **BART**"
      ],
      "metadata": {
        "id": "mW5Oyn6vHG9F"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LybaJNDk8cxB"
      },
      "outputs": [],
      "source": [
        "!pip install transformers[sentencepiece] datasets sacrebleu rouge_score py7zr -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vPJUeeG8vPt",
        "outputId": "3088a140-877e-4b5e-a07a-1a137dc7ba66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tue Nov  7 06:04:35 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   53C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, set_seed\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from datasets import load_dataset\n",
        "import pandas as pd\n",
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "\n",
        "import nltk\n",
        "from nltk.tokenize import sent_tokenize\n",
        "\n",
        "from tqdm import tqdm\n",
        "import torch\n",
        "\n",
        "nltk.download(\"punkt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VSFJ-_F_8xXW",
        "outputId": "41ca8504-1774-4e19-eacf-525609805538"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import torch\n",
        "# from transformers import BertTokenizer, BertLMHeadModel\n",
        "# from torch import nn\n",
        "\n",
        "# device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "# model_ckpt = \"bert-base-uncased\"\n",
        "\n",
        "# tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "# model_bert = BertLMHeadModel.from_pretrained(\"bert-base-uncased\")"
      ],
      "metadata": {
        "id": "gY7YCjJyLIoJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model_ckpt = \"MohamedZaitoon/bart-fine-tune\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
        "\n",
        "model_bart = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt).to(device)"
      ],
      "metadata": {
        "id": "oGZkI2m_8z7K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_batch_sized_chunks(list_of_elements, batch_size):\n",
        "    \"\"\"split the dataset into smaller batches that we can process simultaneously\n",
        "    Yield successive batch-sized chunks from list_of_elements.\"\"\"\n",
        "    for i in range(0, len(list_of_elements), batch_size):\n",
        "        yield list_of_elements[i : i + batch_size]"
      ],
      "metadata": {
        "id": "0s4OroTE82af"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_metric_on_test_ds(dataset, metric, model, tokenizer,\n",
        "                               batch_size=16, device=device,\n",
        "                               column_text=\"input\",\n",
        "                               column_summary=\"output\"):\n",
        "    article_batches = list(generate_batch_sized_chunks(dataset[column_text], batch_size))\n",
        "    target_batches = list(generate_batch_sized_chunks(dataset[column_summary], batch_size))\n",
        "\n",
        "    for article_batch, target_batch in tqdm(\n",
        "        zip(article_batches, target_batches), total=len(article_batches)):\n",
        "\n",
        "        inputs = tokenizer(article_batch, max_length=1024,  truncation=True,\n",
        "                        padding=\"max_length\", return_tensors=\"pt\")\n",
        "\n",
        "        summaries = model.generate(input_ids=inputs[\"input_ids\"].to(device),\n",
        "                         attention_mask=inputs[\"attention_mask\"].to(device),\n",
        "                         length_penalty=0.8, num_beams=8, max_length=128)\n",
        "        ''' parameter for length penalty ensures that the model does not generate sequences that are too long. '''\n",
        "\n",
        "        # Finally, we decode the generated texts,\n",
        "        # replace the  token, and add the decoded texts with the references to the metric.\n",
        "        decoded_summaries = [tokenizer.decode(s, skip_special_tokens=True,\n",
        "                                clean_up_tokenization_spaces=True)\n",
        "               for s in summaries]\n",
        "\n",
        "        decoded_summaries = [d.replace(\"\", \" \") for d in decoded_summaries]\n",
        "\n",
        "\n",
        "        metric.add_batch(predictions=decoded_summaries, references=target_batch)\n",
        "\n",
        "    #  Finally compute and return the ROUGE scores.\n",
        "    score = metric.compute()\n",
        "    return score"
      ],
      "metadata": {
        "id": "cWugZIn584vr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LOAD DATA**\n"
      ],
      "metadata": {
        "id": "RegNJ3BE896c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Link: https://huggingface.co/datasets/samsum"
      ],
      "metadata": {
        "id": "NsMu9h-18-qO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset_name = \"medalpaca/medical_meadow_cord19\"\n",
        "\n",
        "data= load_dataset(dataset_name,split='train')\n",
        "num_samples = 500\n",
        "dataset_samsum = data.select(range(num_samples)).train_test_split( test_size=0.2)\n",
        "\n",
        "\n",
        "print(f\"Features: {dataset_samsum['train'].column_names}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgfNCi3I9BOJ",
        "outputId": "b711455f-f92d-43b2-c650-1140819f399f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features: ['input', 'instruction', 'output']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluating base model BART on the dataset**"
      ],
      "metadata": {
        "id": "J0Y3uWFz9Gwa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_samsum['test'][1]['input']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "A5jiMk8n9DhJ",
        "outputId": "5257e83d-d935-45f8-d329-a5fe0a7b3768"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'BACKGROUND AND AIMS: Vaccine hesitancy is an ongoing major challenge. We aimed to assess the uptake and hesitancy of the COVID-19 vaccination. METHODS: A short online survey was posted between April 12 to July 31, 2021 targeted at health and social care workers (HCWs) across the globe. RESULTS: 275 from 37 countries responded. Most were hospital or primary care physicians or nurses, 59% women, aged 18–60 years, and 21% had chronic conditions with most prevalent being diabetes, hypertension, and asthma. We found that most HCWs (93%) had taken or willing to receive the COVID-19 vaccine. While 7% were vaccine hesitant (mainly women aged 30–39 years), respondents main concerns was the safety or potential side effects. Vaccine willing respondents raised concerns of unequal access to the COVID-19 vaccination in some countries, and highlighted that the only solution to overcoming COVID-19 infections was the vaccine booster doses given annually and free mass vaccination. CONCLUSIONS: This study found that the majority of the frontline HCWs are willing to receive the COVID-19 vaccine. Further promotion of the COVID-19 vaccine would reassure and persuade HCWs to become vaccinated.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = pipeline('summarization', model = model_ckpt )\n",
        "\n",
        "pipe_out = pipe(dataset_samsum['test'][1]['input'] )\n",
        "\n",
        "print(pipe_out)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CicQ6UO19M6l",
        "outputId": "89c71155-431d-463e-df7b-beb7f7d5df1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'summary_text': 'Survey was posted between April 12 to July 31, 2021 targeted at health and social care workers (HCWs) across the globe .\\nMost HCWs (93%) had taken or willing to receive the COVID-19 vaccine .\\n7% were vaccine hesitant (mainly women aged 30-39 years)'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pipe_out[0]['summary_text'].replace(\" .<n>\", \".\\n\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOlsyqDT9PWn",
        "outputId": "ea598dee-3682-4ef1-b209-f231886617b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Survey was posted between April 12 to July 31, 2021 targeted at health and social care workers (HCWs) across the globe .\n",
            "Most HCWs (93%) had taken or willing to receive the COVID-19 vaccine .\n",
            "7% were vaccine hesitant (mainly women aged 30-39 years)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bert_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTk9iINy9PHL",
        "outputId": "844a705c-61ad-47f9-c054-f0143382498c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: bert_score in /usr/local/lib/python3.10/dist-packages (0.3.13)\n",
            "Requirement already satisfied: torch>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from bert_score) (2.1.0+cu118)\n",
            "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from bert_score) (1.5.3)\n",
            "Requirement already satisfied: transformers>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from bert_score) (4.35.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bert_score) (1.23.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from bert_score) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.31.1 in /usr/local/lib/python3.10/dist-packages (from bert_score) (4.66.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from bert_score) (3.7.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from bert_score) (23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->bert_score) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.1->bert_score) (2023.3.post1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (3.12.4)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.0.0->bert_score) (2.1.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.17.3)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (6.0.1)\n",
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            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.14.1)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=3.0.0->bert_score) (0.4.0)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (4.43.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->bert_score) (3.1.1)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->bert_score) (2023.7.22)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=1.0.1->bert_score) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.0.0->bert_score) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.0.0->bert_score) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_metric = load_metric('rouge')\n",
        "\n",
        "score = calculate_metric_on_test_ds(dataset_samsum['test'], rouge_metric, model_bart, tokenizer, column_text = 'input', column_summary='output', batch_size=8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1r9IaAy9WDd",
        "outputId": "b7a3b631-9344-4aad-e7a5-e36821c44f3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-60be374f5b19>:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  rouge_metric = load_metric('rouge')\n",
            "100%|██████████| 13/13 [02:10<00:00, 10.07s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
        "rouge_dict = dict((rn, score[rn].mid.fmeasure ) for rn in rouge_names )\n",
        "\n",
        "pd.DataFrame(rouge_dict, index = ['bart'])"
      ],
      "metadata": {
        "id": "mG9zNT3k9XW_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "8d6e95a8-19eb-4936-ad7f-51944a81b51e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        rouge1    rouge2    rougeL  rougeLsum\n",
              "bart  0.004602  0.000162  0.004623   0.004578"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>rouge1</th>\n",
              "      <th>rouge2</th>\n",
              "      <th>rougeL</th>\n",
              "      <th>rougeLsum</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bart</th>\n",
              "      <td>0.004602</td>\n",
              "      <td>0.000162</td>\n",
              "      <td>0.004623</td>\n",
              "      <td>0.004578</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7329d492-2c4c-4fd7-bfca-d3e66b6a19eb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7329d492-2c4c-4fd7-bfca-d3e66b6a19eb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7329d492-2c4c-4fd7-bfca-d3e66b6a19eb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Histogram**"
      ],
      "metadata": {
        "id": "M_gBNffq9cfD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dialogue_token_len = len([tokenizer.encode(s) for s in dataset_samsum['train']['input']])\n",
        "\n",
        "summary_token_len = len([tokenizer.encode(s) for s in dataset_samsum['train']['output']])\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
        "axes[0].hist(dialogue_token_len, bins = 20, color = 'C0', edgecolor = 'C0' )\n",
        "axes[0].set_title(\"Dialogue Token Length\")\n",
        "axes[0].set_xlabel(\"Length\")\n",
        "axes[0].set_ylabel(\"Count\")\n",
        "\n",
        "axes[1].hist(summary_token_len, bins = 20, color = 'C0', edgecolor = 'C0' )\n",
        "axes[1].set_title(\"Summary Token Length\")\n",
        "axes[1].set_xlabel(\"Length\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "9XM427cT9Zkc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "46148350-5b77-4281-df80-07a29d9769e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (1161 > 1024). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_examples_to_features(example_batch):\n",
        "    input_encodings = tokenizer(example_batch['input'] , max_length = 1024, truncation = True )\n",
        "\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        target_encodings = tokenizer(example_batch['output'], max_length = 128, truncation = True )\n",
        "\n",
        "    return {\n",
        "        'input_ids' : input_encodings['input_ids'],\n",
        "        'attention_mask': input_encodings['attention_mask'],\n",
        "        'labels': target_encodings['input_ids']\n",
        "    }\n",
        "\n",
        "dataset_samsum_pt = dataset_samsum.map(convert_examples_to_features, batched = True)"
      ],
      "metadata": {
        "id": "xeoBy6Ys9gzv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "dcbdee1bb74c438c8295255de625eece",
            "7dedf572b6124e10b7306eacb8e8e7ae",
            "db75a04ea34148428fbe3037b2667f0c",
            "871ad8baf4a74fa4a14e03590350c497",
            "3efd249d95a4434092fcd6830eb62fbc",
            "741beee1b36748f1a7ebc5ca3c50d5d8",
            "86d61c4d4a29492999f161c679b2bc77",
            "53242c26bc304ac8ae8b5fbbe094b886",
            "2c8f35d3992d44e6b26d7b15ef114f76",
            "3e37d3ee9ccf4375b8933ee40a432c0f",
            "5067c89e49f0485f85675aaac0f4c75a",
            "1d55b540a8434d7c8f1b22ce305bee53",
            "dfb9751ad5364e6899a340d4687173d0",
            "e09214f73cfc491b9fe3452272b9cc79",
            "c81213c3e35c483199fa3a0652a3c408",
            "2ff583a2228f4e2ea00f5463cacca53a",
            "22f1bbdd361e4ff397dad9a3d26cacee",
            "115d78860b22475aaacacf0a763b28ab",
            "963f107bf617466b94ce44eda11b0adc",
            "1fd8864264954c4e93a822d3880e7ac5",
            "e75735ad8de245adba47fb00875e87e2",
            "8c2a817e6b1c419cbe26722e96421ab9"
          ]
        },
        "outputId": "89c46a64-db92-47da-b135-8389bcf9d2aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/400 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dcbdee1bb74c438c8295255de625eece"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3856: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d55b540a8434d7c8f1b22ce305bee53"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[torch]"
      ],
      "metadata": {
        "id": "HAS1U-Ov9kGp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9559545-b4a5-49b6-bea1-50edf2783268"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.35.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.12.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.17.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.14.1)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.4.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (4.66.1)\n",
            "Requirement already satisfied: torch!=1.12.0,>=1.10 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.1.0+cu118)\n",
            "Requirement already satisfied: accelerate>=0.20.3 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.24.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.20.3->transformers[torch]) (5.9.5)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers[torch]) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers[torch]) (4.5.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate -U"
      ],
      "metadata": {
        "id": "ytG2xZ4S9nfm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e94b70b1-3db4-4863-cd2b-208f0d00e677"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.24.1)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu118)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.17.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.12.4)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "seq2seq_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model_bart)"
      ],
      "metadata": {
        "id": "K2QOeotB9qJi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, Trainer\n",
        "\n",
        "trainer_args = TrainingArguments(\n",
        "    output_dir='bart-samsum', num_train_epochs=1, warmup_steps=500,\n",
        "    per_device_train_batch_size=1, per_device_eval_batch_size=1,\n",
        "    weight_decay=0.01, logging_steps=10,\n",
        "    evaluation_strategy='steps', eval_steps=500, save_steps=1e6,\n",
        "    gradient_accumulation_steps=16\n",
        ")"
      ],
      "metadata": {
        "id": "tSXaiA0N9s0F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(model=model_bart, args=trainer_args,\n",
        "                  tokenizer=tokenizer, data_collator=seq2seq_data_collator,\n",
        "                  train_dataset=dataset_samsum_pt[\"train\"],\n",
        "                  eval_dataset=dataset_samsum_pt[\"test\"])"
      ],
      "metadata": {
        "id": "ePO1b5-k9vWT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "uM7e8k149yQ1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "outputId": "f4d269e8-1767-46c1-e019-2e6070fc1a9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BartTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='25' max='25' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [25/25 01:23, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=25, training_loss=3.030052490234375, metrics={'train_runtime': 86.4647, 'train_samples_per_second': 4.626, 'train_steps_per_second': 0.289, 'total_flos': 255221009473536.0, 'train_loss': 3.030052490234375, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = calculate_metric_on_test_ds(\n",
        "    dataset_samsum['test'], rouge_metric, trainer.model, tokenizer, batch_size = 2, column_text = 'input', column_summary= 'output'\n",
        ")\n",
        "\n",
        "rouge_dict = dict((rn, score[rn].mid.fmeasure ) for rn in rouge_names )\n",
        "\n",
        "pd.DataFrame(rouge_dict, index = [f'bert'] )"
      ],
      "metadata": {
        "id": "OBSNjCM590_3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "outputId": "dc7e4bfe-e02d-4d42-e298-d130bd524555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 50/50 [02:16<00:00,  2.72s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        rouge1    rouge2    rougeL  rougeLsum\n",
              "bert  0.006755  0.000387  0.006798   0.006733"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b7b6dc5f-1ed2-44dd-9335-5bc5a099c9ec\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rouge1</th>\n",
              "      <th>rouge2</th>\n",
              "      <th>rougeL</th>\n",
              "      <th>rougeLsum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>bert</th>\n",
              "      <td>0.006755</td>\n",
              "      <td>0.000387</td>\n",
              "      <td>0.006798</td>\n",
              "      <td>0.006733</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7b6dc5f-1ed2-44dd-9335-5bc5a099c9ec')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b7b6dc5f-1ed2-44dd-9335-5bc5a099c9ec button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b7b6dc5f-1ed2-44dd-9335-5bc5a099c9ec');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Save model\n",
        "model_bart.save_pretrained(\"MohamedZaitoon/bart-fine-tune\")"
      ],
      "metadata": {
        "id": "0-SbLS8g93q2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Save tokenizer\n",
        "tokenizer.save_pretrained(\"tokenizer\")"
      ],
      "metadata": {
        "id": "PSrxpFIx984p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "792cacf2-e279-4bbe-c5e3-5000cf40bfc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('tokenizer/tokenizer_config.json',\n",
              " 'tokenizer/special_tokens_map.json',\n",
              " 'tokenizer/vocab.json',\n",
              " 'tokenizer/merges.txt',\n",
              " 'tokenizer/added_tokens.json',\n",
              " 'tokenizer/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TEST**"
      ],
      "metadata": {
        "id": "0bWRsOj5-B7M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sample_text = dataset_samsum[\"test\"][0][\"input\"]\n",
        "\n",
        "reference = dataset_samsum[\"test\"][0][\"output\"]"
      ],
      "metadata": {
        "id": "hjDwtsL9-C79"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gen_kwargs = {\"length_penalty\": 0.8, \"num_beams\":8, \"max_length\": 128}\n",
        "\n",
        "pipe = pipeline(\"summarization\", model=\"MohamedZaitoon/bart-fine-tune\",tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "YtYo9YXV-GbD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Dialogue:\")\n",
        "print(sample_text)\n",
        "\n",
        "\n",
        "print(\"\\nReference Summary:\")\n",
        "print(reference)\n",
        "\n",
        "\n",
        "print(\"\\nModel Summary:\")\n",
        "print(pipe(sample_text, **gen_kwargs)[0][\"summary_text\"])"
      ],
      "metadata": {
        "id": "-B_7HvS4-IkP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "315c0f87-7010-49f4-bc10-52ab68123e76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dialogue:\n",
            "Children with sickle cell disease can develop life-threatening and painful crises that require prompt assessment and efficient management by healthcare professionals in the emergency or acute care setting. Due to migration patterns and improved survival rates in high-prevalence countries, there is an increased tendency to encounter these patients across the UK. These factors warrant regular revisions in sickle cell crisis management, along with education for medical personnel and patients to improve clinical care and patient management. The focus of this article is on the initial assessment and management of acute paediatric sickle cell complications in the emergency setting. Specific case studies, including acute pain crises, trauma, splenic sequestration, aplastic crises, acute chest syndrome, infection, avascular necrosis, osteomyelitis and stroke, are discussed. Due to the current COVID-19 pandemic, we have also reviewed specific concerns around this patient group.\n",
            "\n",
            "Reference Summary:\n",
            "Fifteen-minute consultation: Recognition of sickle cell crises in the paediatric emergency department\n",
            "\n",
            "Model Summary:\n",
            "Children with sickle cell disease can develop life-threatening and painful crises that require prompt assessment and efficient management by healthcare professionals in the emergency or acute care setting .\n",
            "Due to migration patterns and improved survival rates in high-prevalence countries, there is an increased tendency to encounter these patients across the UK .\n"
          ]
        }
      ]
    }
  ]
}