Transformers_medical.ipynb

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            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.12.1 tokenizers-0.13.2 transformers-4.26.1\n"
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
            "Installing collected packages: xxhash, dill, responses, multiprocess, datasets\n",
            "Successfully installed datasets-2.10.1 dill-0.3.6 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "VlO5aNKtgmo-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_datasets as tfdata"
      ],
      "metadata": {
        "id": "dKQv-VG1gtZv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are two ways to work with models that have reached mainstream adoption: Tensorflow (Keras) and PyTorch. Here we are working with the Tensorflow Library"
      ],
      "metadata": {
        "id": "MDUECwE9spcR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257,
          "referenced_widgets": [
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            "4d4878c0515d4d11948f52fb6603ca9d"
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        },
        "id": "l8Xea_garRI0",
        "outputId": "defe0dcb-f0d5-400d-d8d4-a61923aa4a43"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "934193bd27b54415916c9c1983091b7f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de7d1088f3484223b16b2ccb1126e99e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5aa1e098805f4d05943cf0ce201fbd61"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"tf_model.h5\";:   0%|          | 0.00/536M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bfc8a02712b4ecb8d0ab615c801a55c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['nsp___cls', 'mlm___cls']\n",
            "- This IS expected if you are initializing TFBertModel 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 TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFBertModel were initialized from the model checkpoint at bert-base-uncased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "from transformers import BertTokenizer, TFBertModel\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = TFBertModel.from_pretrained(\"bert-base-uncased\")\n",
        "text = \"Replace me by any text you'd like.\" #change this if you want to see a different output!\n",
        "encoded_input = tokenizer(text, return_tensors='tf')\n",
        "output = model(encoded_input)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "An example of the sentence \"Replace me by any text you'd like\" - feel free to change it. What you should NOT change however, is the Tokenizer. The tokenizer comes with the model and it should only be used with that model. Keep that in mind while trying out other models."
      ],
      "metadata": {
        "id": "U7rteemrs8Ng"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zUvsiRfrbo2",
        "outputId": "c4d698ce-6add-4f3d-e796-6497e5e039b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TFBaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=<tf.Tensor: shape=(1, 12, 768), dtype=float32, numpy=\n",
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              "         -0.28439468,  0.4578122 ],\n",
              "        [ 0.53687984, -0.23298709,  0.17540604, ...,  0.5545705 ,\n",
              "          0.49828747, -0.00224124],\n",
              "        [ 0.30028492, -0.34752145,  0.12078432, ..., -0.45572907,\n",
              "          0.3285413 ,  0.87741655],\n",
              "        ...,\n",
              "        [ 0.38045487,  0.12035119,  0.82856995, ..., -0.8627312 ,\n",
              "         -0.5952204 ,  0.04663449],\n",
              "        [-0.02505059, -0.71751815, -0.69520533, ...,  0.07620818,\n",
              "         -0.66668653, -0.339958  ],\n",
              "        [ 0.75341094,  0.23900357,  0.07204142, ...,  0.2466754 ,\n",
              "         -0.6457954 , -0.32167816]]], dtype=float32)>, pooler_output=<tf.Tensor: shape=(1, 768), dtype=float32, numpy=\n",
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              "        -8.31243455e-01,  8.52295458e-01, -9.39809918e-01,\n",
              "         9.49963987e-01, -9.33818460e-01, -9.85114992e-01,\n",
              "        -3.49424720e-01,  8.43712330e-01,  9.37445641e-01,\n",
              "        -5.15655756e-01, -8.98903131e-01,  5.19453645e-01,\n",
              "        -8.79606426e-01,  9.97923732e-01,  8.75234246e-01,\n",
              "        -8.27510595e-01, -9.16916528e-04,  6.01060212e-01,\n",
              "        -9.18451786e-01, -7.39734113e-01,  9.22796786e-01]], dtype=float32)>, past_key_values=None, hidden_states=None, attentions=None, cross_attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have to specify the path to the data\n"
      ],
      "metadata": {
        "id": "ybc5o7ZRViub"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "PATH = '/content/archive.zip'"
      ],
      "metadata": {
        "id": "vq2H6_dnD3WG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feel free to change the path for the zipfile."
      ],
      "metadata": {
        "id": "O5cvtxRJ0pH8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "841plrjRzyEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "zf = zipfile.ZipFile(PATH) \n",
        "df = pd.read_csv(zf.open('mtsamples.csv'))"
      ],
      "metadata": {
        "id": "BwAVYbtonj0u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "80O_GDSH0uin",
        "outputId": "31490612-2e51-4bdd-c943-e0d2c365723a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0                                        description  \\\n",
              "0              0   A 23-year-old white female presents with comp...   \n",
              "1              1           Consult for laparoscopic gastric bypass.   \n",
              "2              2           Consult for laparoscopic gastric bypass.   \n",
              "3              3                             2-D M-Mode. Doppler.     \n",
              "4              4                                 2-D Echocardiogram   \n",
              "...          ...                                                ...   \n",
              "4994        4994   Patient having severe sinusitis about two to ...   \n",
              "4995        4995   This is a 14-month-old baby boy Caucasian who...   \n",
              "4996        4996   A female for a complete physical and follow u...   \n",
              "4997        4997   Mother states he has been wheezing and coughing.   \n",
              "4998        4998   Acute allergic reaction, etiology uncertain, ...   \n",
              "\n",
              "                medical_specialty                                sample_name  \\\n",
              "0            Allergy / Immunology                         Allergic Rhinitis    \n",
              "1                      Bariatrics   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                      Bariatrics   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3      Cardiovascular / Pulmonary                    2-D Echocardiogram - 1    \n",
              "4      Cardiovascular / Pulmonary                    2-D Echocardiogram - 2    \n",
              "...                           ...                                        ...   \n",
              "4994         Allergy / Immunology                         Chronic Sinusitis    \n",
              "4995         Allergy / Immunology      Kawasaki Disease - Discharge Summary    \n",
              "4996         Allergy / Immunology                        Followup on Asthma    \n",
              "4997         Allergy / Immunology                    Asthma in a 5-year-old    \n",
              "4998         Allergy / Immunology                Allergy Evaluation Consult    \n",
              "\n",
              "                                          transcription  \\\n",
              "0     SUBJECTIVE:,  This 23-year-old white female pr...   \n",
              "1     PAST MEDICAL HISTORY:, He has difficulty climb...   \n",
              "2     HISTORY OF PRESENT ILLNESS: , I have seen ABC ...   \n",
              "3     2-D M-MODE: , ,1.  Left atrial enlargement wit...   \n",
              "4     1.  The left ventricular cavity size and wall ...   \n",
              "...                                                 ...   \n",
              "4994  HISTORY:,  I had the pleasure of meeting and e...   \n",
              "4995  ADMITTING DIAGNOSIS: , Kawasaki disease.,DISCH...   \n",
              "4996  SUBJECTIVE: , This is a 42-year-old white fema...   \n",
              "4997  CHIEF COMPLAINT: , This 5-year-old male presen...   \n",
              "4998  HISTORY: , A 34-year-old male presents today s...   \n",
              "\n",
              "                                               keywords  \n",
              "0     allergy / immunology, allergic rhinitis, aller...  \n",
              "1     bariatrics, laparoscopic gastric bypass, weigh...  \n",
              "2     bariatrics, laparoscopic gastric bypass, heart...  \n",
              "3     cardiovascular / pulmonary, 2-d m-mode, dopple...  \n",
              "4     cardiovascular / pulmonary, 2-d, doppler, echo...  \n",
              "...                                                 ...  \n",
              "4994                                                NaN  \n",
              "4995  allergy / immunology, mucous membranes, conjun...  \n",
              "4996                                                NaN  \n",
              "4997                                                NaN  \n",
              "4998                                                NaN  \n",
              "\n",
              "[4999 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e1e9c98-2b1b-4943-a86b-2e3c54bf59f1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
              "      <th>sample_name</th>\n",
              "      <th>transcription</th>\n",
              "      <th>keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A 23-year-old white female presents with comp...</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Allergic Rhinitis</td>\n",
              "      <td>SUBJECTIVE:,  This 23-year-old white female pr...</td>\n",
              "      <td>allergy / immunology, allergic rhinitis, aller...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
              "      <td>PAST MEDICAL HISTORY:, He has difficulty climb...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, weigh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>Bariatrics</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
              "      <td>HISTORY OF PRESENT ILLNESS: , I have seen ABC ...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, heart...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2-D M-Mode. Doppler.</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 1</td>\n",
              "      <td>2-D M-MODE: , ,1.  Left atrial enlargement wit...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d m-mode, dopple...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2-D Echocardiogram</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>1.  The left ventricular cavity size and wall ...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d, doppler, echo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4994</th>\n",
              "      <td>4994</td>\n",
              "      <td>Patient having severe sinusitis about two to ...</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Chronic Sinusitis</td>\n",
              "      <td>HISTORY:,  I had the pleasure of meeting and e...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>4995</td>\n",
              "      <td>This is a 14-month-old baby boy Caucasian who...</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Kawasaki Disease - Discharge Summary</td>\n",
              "      <td>ADMITTING DIAGNOSIS: , Kawasaki disease.,DISCH...</td>\n",
              "      <td>allergy / immunology, mucous membranes, conjun...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>4996</td>\n",
              "      <td>A female for a complete physical and follow u...</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Followup on Asthma</td>\n",
              "      <td>SUBJECTIVE: , This is a 42-year-old white fema...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>4997</td>\n",
              "      <td>Mother states he has been wheezing and coughing.</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Asthma in a 5-year-old</td>\n",
              "      <td>CHIEF COMPLAINT: , This 5-year-old male presen...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>4998</td>\n",
              "      <td>Acute allergic reaction, etiology uncertain, ...</td>\n",
              "      <td>Allergy / Immunology</td>\n",
              "      <td>Allergy Evaluation Consult</td>\n",
              "      <td>HISTORY: , A 34-year-old male presents today s...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4999 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e1e9c98-2b1b-4943-a86b-2e3c54bf59f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
              "    [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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7e1e9c98-2b1b-4943-a86b-2e3c54bf59f1 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-7e1e9c98-2b1b-4943-a86b-2e3c54bf59f1');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "typWKTcl0lum"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we look at the data more clearly, we can see different labels: What we would like to do now is to get the transcription to map to the medical_specialty. We want to finetune the BERT Model with the data we have obtained from Kaggle to predict the category for us. "
      ],
      "metadata": {
        "id": "fu88Sx472pgy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10,5))\n",
        "plot = sns.countplot(data=df,\n",
        "                   x= 'medical_specialty')\n",
        "plot.set_xticklabels(plot.get_xticklabels(), rotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "qtKC_sphhhbS",
        "outputId": "c041579a-9b54-4104-bd7c-57486a796ac2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, ' Allergy / Immunology'),\n",
              " Text(1, 0, ' Bariatrics'),\n",
              " Text(2, 0, ' Cardiovascular / Pulmonary'),\n",
              " Text(3, 0, ' Neurology'),\n",
              " Text(4, 0, ' Dentistry'),\n",
              " Text(5, 0, ' Urology'),\n",
              " Text(6, 0, ' General Medicine'),\n",
              " Text(7, 0, ' Surgery'),\n",
              " Text(8, 0, ' Speech - Language'),\n",
              " Text(9, 0, ' SOAP / Chart / Progress Notes'),\n",
              " Text(10, 0, ' Sleep Medicine'),\n",
              " Text(11, 0, ' Rheumatology'),\n",
              " Text(12, 0, ' Radiology'),\n",
              " Text(13, 0, ' Psychiatry / Psychology'),\n",
              " Text(14, 0, ' Podiatry'),\n",
              " Text(15, 0, ' Physical Medicine - Rehab'),\n",
              " Text(16, 0, ' Pediatrics - Neonatal'),\n",
              " Text(17, 0, ' Pain Management'),\n",
              " Text(18, 0, ' Orthopedic'),\n",
              " Text(19, 0, ' Ophthalmology'),\n",
              " Text(20, 0, ' Office Notes'),\n",
              " Text(21, 0, ' Obstetrics / Gynecology'),\n",
              " Text(22, 0, ' Neurosurgery'),\n",
              " Text(23, 0, ' Nephrology'),\n",
              " Text(24, 0, ' Letters'),\n",
              " Text(25, 0, ' Lab Medicine - Pathology'),\n",
              " Text(26, 0, ' IME-QME-Work Comp etc.'),\n",
              " Text(27, 0, ' Hospice - Palliative Care'),\n",
              " Text(28, 0, ' Hematology - Oncology'),\n",
              " Text(29, 0, ' Gastroenterology'),\n",
              " Text(30, 0, ' ENT - Otolaryngology'),\n",
              " Text(31, 0, ' Endocrinology'),\n",
              " Text(32, 0, ' Emergency Room Reports'),\n",
              " Text(33, 0, ' Discharge Summary'),\n",
              " Text(34, 0, ' Diets and Nutritions'),\n",
              " Text(35, 0, ' Dermatology'),\n",
              " Text(36, 0, ' Cosmetic / Plastic Surgery'),\n",
              " Text(37, 0, ' Consult - History and Phy.'),\n",
              " Text(38, 0, ' Chiropractic'),\n",
              " Text(39, 0, ' Autopsy')]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "df['medical_specialty'] = encoder.fit_transform(df['medical_specialty'])"
      ],
      "metadata": {
        "id": "_o9vbh6X1IJT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We changed the Medical Specialty to numbers"
      ],
      "metadata": {
        "id": "ojA5dYxeA42l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "gfW0ldlj1YNp",
        "outputId": "c23daaac-7954-4798-b3f2-72ba4d9eb51c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0                                        description  \\\n",
              "0              0   A 23-year-old white female presents with comp...   \n",
              "1              1           Consult for laparoscopic gastric bypass.   \n",
              "2              2           Consult for laparoscopic gastric bypass.   \n",
              "3              3                             2-D M-Mode. Doppler.     \n",
              "4              4                                 2-D Echocardiogram   \n",
              "...          ...                                                ...   \n",
              "4994        4994   Patient having severe sinusitis about two to ...   \n",
              "4995        4995   This is a 14-month-old baby boy Caucasian who...   \n",
              "4996        4996   A female for a complete physical and follow u...   \n",
              "4997        4997   Mother states he has been wheezing and coughing.   \n",
              "4998        4998   Acute allergic reaction, etiology uncertain, ...   \n",
              "\n",
              "      medical_specialty                                sample_name  \\\n",
              "0                     0                         Allergic Rhinitis    \n",
              "1                     2   Laparoscopic Gastric Bypass Consult - 2    \n",
              "2                     2   Laparoscopic Gastric Bypass Consult - 1    \n",
              "3                     3                    2-D Echocardiogram - 1    \n",
              "4                     3                    2-D Echocardiogram - 2    \n",
              "...                 ...                                        ...   \n",
              "4994                  0                         Chronic Sinusitis    \n",
              "4995                  0      Kawasaki Disease - Discharge Summary    \n",
              "4996                  0                        Followup on Asthma    \n",
              "4997                  0                    Asthma in a 5-year-old    \n",
              "4998                  0                Allergy Evaluation Consult    \n",
              "\n",
              "                                          transcription  \\\n",
              "0     SUBJECTIVE:,  This 23-year-old white female pr...   \n",
              "1     PAST MEDICAL HISTORY:, He has difficulty climb...   \n",
              "2     HISTORY OF PRESENT ILLNESS: , I have seen ABC ...   \n",
              "3     2-D M-MODE: , ,1.  Left atrial enlargement wit...   \n",
              "4     1.  The left ventricular cavity size and wall ...   \n",
              "...                                                 ...   \n",
              "4994  HISTORY:,  I had the pleasure of meeting and e...   \n",
              "4995  ADMITTING DIAGNOSIS: , Kawasaki disease.,DISCH...   \n",
              "4996  SUBJECTIVE: , This is a 42-year-old white fema...   \n",
              "4997  CHIEF COMPLAINT: , This 5-year-old male presen...   \n",
              "4998  HISTORY: , A 34-year-old male presents today s...   \n",
              "\n",
              "                                               keywords  \n",
              "0     allergy / immunology, allergic rhinitis, aller...  \n",
              "1     bariatrics, laparoscopic gastric bypass, weigh...  \n",
              "2     bariatrics, laparoscopic gastric bypass, heart...  \n",
              "3     cardiovascular / pulmonary, 2-d m-mode, dopple...  \n",
              "4     cardiovascular / pulmonary, 2-d, doppler, echo...  \n",
              "...                                                 ...  \n",
              "4994                                                NaN  \n",
              "4995  allergy / immunology, mucous membranes, conjun...  \n",
              "4996                                                NaN  \n",
              "4997                                                NaN  \n",
              "4998                                                NaN  \n",
              "\n",
              "[4999 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-deeee839-3135-4253-b4f3-1b87b51b7ccc\">\n",
              "    <div 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>Unnamed: 0</th>\n",
              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
              "      <th>sample_name</th>\n",
              "      <th>transcription</th>\n",
              "      <th>keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>A 23-year-old white female presents with comp...</td>\n",
              "      <td>0</td>\n",
              "      <td>Allergic Rhinitis</td>\n",
              "      <td>SUBJECTIVE:,  This 23-year-old white female pr...</td>\n",
              "      <td>allergy / immunology, allergic rhinitis, aller...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>2</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 2</td>\n",
              "      <td>PAST MEDICAL HISTORY:, He has difficulty climb...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, weigh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Consult for laparoscopic gastric bypass.</td>\n",
              "      <td>2</td>\n",
              "      <td>Laparoscopic Gastric Bypass Consult - 1</td>\n",
              "      <td>HISTORY OF PRESENT ILLNESS: , I have seen ABC ...</td>\n",
              "      <td>bariatrics, laparoscopic gastric bypass, heart...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2-D M-Mode. Doppler.</td>\n",
              "      <td>3</td>\n",
              "      <td>2-D Echocardiogram - 1</td>\n",
              "      <td>2-D M-MODE: , ,1.  Left atrial enlargement wit...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d m-mode, dopple...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2-D Echocardiogram</td>\n",
              "      <td>3</td>\n",
              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>1.  The left ventricular cavity size and wall ...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d, doppler, echo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4994</th>\n",
              "      <td>4994</td>\n",
              "      <td>Patient having severe sinusitis about two to ...</td>\n",
              "      <td>0</td>\n",
              "      <td>Chronic Sinusitis</td>\n",
              "      <td>HISTORY:,  I had the pleasure of meeting and e...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>4995</td>\n",
              "      <td>This is a 14-month-old baby boy Caucasian who...</td>\n",
              "      <td>0</td>\n",
              "      <td>Kawasaki Disease - Discharge Summary</td>\n",
              "      <td>ADMITTING DIAGNOSIS: , Kawasaki disease.,DISCH...</td>\n",
              "      <td>allergy / immunology, mucous membranes, conjun...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>4996</td>\n",
              "      <td>A female for a complete physical and follow u...</td>\n",
              "      <td>0</td>\n",
              "      <td>Followup on Asthma</td>\n",
              "      <td>SUBJECTIVE: , This is a 42-year-old white fema...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>4997</td>\n",
              "      <td>Mother states he has been wheezing and coughing.</td>\n",
              "      <td>0</td>\n",
              "      <td>Asthma in a 5-year-old</td>\n",
              "      <td>CHIEF COMPLAINT: , This 5-year-old male presen...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>4998</td>\n",
              "      <td>Acute allergic reaction, etiology uncertain, ...</td>\n",
              "      <td>0</td>\n",
              "      <td>Allergy Evaluation Consult</td>\n",
              "      <td>HISTORY: , A 34-year-old male presents today s...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4999 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-deeee839-3135-4253-b4f3-1b87b51b7ccc')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-deeee839-3135-4253-b4f3-1b87b51b7ccc 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-deeee839-3135-4253-b4f3-1b87b51b7ccc');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have a look at the distribution of the data.\n",
        "The next steps would include to put it in a different format, which is easier to handle for the ML-Part\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "w8AKLr6vxwdS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset,DatasetDict, Dataset"
      ],
      "metadata": {
        "id": "TbXksBnXgQTA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = Dataset.from_pandas(df)"
      ],
      "metadata": {
        "id": "zl8g37uLfrzN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDm-9QN91EZK",
        "outputId": "eeae4f68-71a7-4c65-85a2-79bae164b7de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'description', 'medical_specialty', 'sample_name', 'transcription', 'keywords'],\n",
              "    num_rows: 4999\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dataset = load_dataset('csv', data_files=PATH, split='train')\n",
        "\n",
        "#dataset"
      ],
      "metadata": {
        "id": "Fjep9oDg_sOT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we delete the Features that we do not want to influence our training."
      ],
      "metadata": {
        "id": "8cgOwTbs95XV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_test_valid = ds.train_test_split()\n",
        "\n",
        "test_valid = train_test_valid['test'].train_test_split()\n",
        "\n",
        "train_test_valid_dataset = DatasetDict({\n",
        "    'train': train_test_valid['train'],\n",
        "    'test': test_valid['test'],\n",
        "    'valid': test_valid['train']\n",
        "    })\n",
        "\n",
        "dataset = train_test_valid_dataset.remove_columns(['sample_name','Unnamed: 0', 'transcription','keywords'])\n",
        "#dataset = train_test_valid_dataset.remove_columns(['medical_specialty', 'sample_name','Unnamed: 0', 'transcription'])\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IG3Q2716ykFq",
        "outputId": "e32c70d3-6391-445c-dc86-d4579f748b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['description', 'medical_specialty'],\n",
              "        num_rows: 3749\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['description', 'medical_specialty'],\n",
              "        num_rows: 313\n",
              "    })\n",
              "    valid: Dataset({\n",
              "        features: ['description', 'medical_specialty'],\n",
              "        num_rows: 937\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "8Ju7w-tu_05k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
      ],
      "metadata": {
        "id": "x2CflEz5_4IH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Vocab size is : {tokenizer.vocab_size}\")\n",
        "\n",
        "print(f\"Model max length is : {tokenizer.model_max_length}\")\n",
        "\n",
        "print(f\"Model input names are: {tokenizer.model_input_names}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYdcGHDBAMf4",
        "outputId": "a9359b8b-eef6-4763-f669-8d9839f8a7c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocab size is : 30522\n",
            "Model max length is : 512\n",
            "Model input names are: ['input_ids', 'token_type_ids', 'attention_mask']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5KdMyn0AWKY",
        "outputId": "2c1ad098-01ea-49a4-d047-cd45aa6986ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'description', 'medical_specialty', 'sample_name', 'transcription', 'keywords'],\n",
              "    num_rows: 4999\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(train_dataset):\n",
        "    return tokenizer(train_dataset['description'], padding='max_length', truncation=True)\n",
        "\n",
        "\n",
        "tokenized_dataset = dataset.map(tokenize_function, batched=True)\n",
        "\n",
        "tokenized_dataset\n",
        "\n",
        "train_dataset = tokenized_dataset['train']\n",
        "eval_dataset = tokenized_dataset['valid']\n",
        "test_dataset = tokenized_dataset['test']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "0065b3623e444410b71c4e660cfe3b41",
            "b4c56a74601b4f0fbe7a1106daf8df7e",
            "734fd02e4ac147399f20eb231d229bf8",
            "fdec748866af4fa6b34dd36b7618a501",
            "20510a464851459ab74b05126fe9addc",
            "2fb54974cbc845558af2dd99d1ce0632",
            "e8a5675b53f841ff9f1fd8fe2b36f9e7",
            "3ffc94b660fd4b1aa611d2906ca746d8",
            "2e2361c28c6d481b9876e30b82016e02",
            "fa8fa932b1b8425485ea736191ad9763",
            "1dc0172ace6b4e5788f5df22f89d4296",
            "12c411b24e7245a5a94e41d1ef3a1eee",
            "4532e34a487f47afb9bca0add9a6a323",
            "aae51544e55048908da07b19153628d5",
            "a5c85adffcc94e30b86b0882a7ad9c0b",
            "421ae85c931445c092be75c529f71700",
            "331b20e6c0434dcd8d595668c2dd84da",
            "074dbb5d65a64076b592091b09119ff6",
            "a1525578fc464f3f86a8ed359f23bdcf",
            "d7848ce22a034f059cd2bc35fd656bd3",
            "b72dad8435d1413587571993c4b54e24",
            "0e5c69601aca4f2e839cb6e7b375b876",
            "e943e94480bf4c19ac9b7c2a69a5e4f1",
            "01a93ca7f19a44418bef4eb9c47cbb59",
            "4d41d087d6884541aa98ffa148faa2f6",
            "5bb743c9c14c4f48bae8243daeff0358",
            "ac68333ae2134148a45a9cc6a886fa57",
            "685292ba748e4f7e857ec08a67d56877",
            "318d785e67cc429d89fc618d39220145",
            "f4792a8b3330495eb8048c7fae4bcb2d",
            "f38c6fb7794843559bfaffcb9df313e0",
            "1974f051d20c4128a036bdabce1bce09",
            "4aed1c823ece420db87c021388306765"
          ]
        },
        "id": "uJ_GTZ3HAZ9S",
        "outputId": "2e777288-e3c1-464a-ef9c-a4c0a6cc26c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3749 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0065b3623e444410b71c4e660cfe3b41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/313 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "12c411b24e7245a5a94e41d1ef3a1eee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/937 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e943e94480bf4c19ac9b7c2a69a5e4f1"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This split is important, as it needs 3 sets.  \n",
        "\n",
        "*   Train\n",
        "*   Test\n",
        "*   Validation\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jaxwCELBBMA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EccBcIkAqW1",
        "outputId": "93053fd1-b213-4924-ec99-8541dddc7cc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['description', 'medical_specialty', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 3749\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_set = train_dataset.remove_columns(['description']).with_format('tensorflow')\n",
        "\n",
        "tf_eval_dataset = eval_dataset.remove_columns(['description']).with_format('tensorflow')\n",
        "\n",
        "tf_test_dataset = test_dataset.remove_columns(['description']).with_format('tensorflow')"
      ],
      "metadata": {
        "id": "kymIA6nwAvbH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nI0i6sbgzPJ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_features = { x: train_set[x] for x in tokenizer.model_input_names  }\n",
        "\n",
        "train_set_for_final_model = tf.data.Dataset.from_tensor_slices((train_features, train_set['medical_specialty'] ))\n",
        "\n",
        "train_set_for_final_model = train_set_for_final_model.shuffle(len(train_set)).batch(8)\n",
        "\n",
        "\n",
        "eval_features = {x: tf_eval_dataset[x] for x in tokenizer.model_input_names}\n",
        "val_set_for_final_model = tf.data.Dataset.from_tensor_slices((eval_features, tf_eval_dataset['medical_specialty']))\n",
        "val_set_for_final_model = val_set_for_final_model.batch(8)\n",
        "\n",
        "test_features = {x: tf_test_dataset[x] for x in tokenizer.model_input_names}\n",
        "test_set_for_final_model = tf.data.Dataset.from_tensor_slices((test_features, tf_test_dataset['medical_specialty']))\n",
        "test_set_for_final_model =test_set_for_final_model.batch(8)"
      ],
      "metadata": {
        "id": "PnN0LZXhBGow"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer,TFAutoModelForSequenceClassification"
      ],
      "metadata": {
        "id": "HRs9DL_PxP6P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = TFAutoModelForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=40)\n",
        "# model = TFAutoModelForSequenceClassification.from_pretrained(\"/mnt/e0ccdbdb-22c3-4d9b-9413-fd976a2e99ae/M1/Code_Org/HF_Models/bert-base-uncased\", num_labels=3)\n",
        "\n",
        "\n",
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),\n",
        "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "    metrics=tf.metrics.SparseCategoricalAccuracy(),\n",
        ")"
      ],
      "metadata": {
        "id": "dYKMNY-YCZez",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99b5b478-80e0-4bf8-e971-93501339a03b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time for us to train"
      ],
      "metadata": {
        "id": "zzUb8YNi8Tcx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_set_for_final_model, validation_data=val_set_for_final_model, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMp1DxRyxgOy",
        "outputId": "24128ae5-b2ff-480d-a86b-2cdaf88de76c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "469/469 [==============================] - 125s 165ms/step - loss: 2.6583 - sparse_categorical_accuracy: 0.2953 - val_loss: 2.3021 - val_sparse_categorical_accuracy: 0.3351\n",
            "Epoch 2/5\n",
            "469/469 [==============================] - 66s 141ms/step - loss: 2.1227 - sparse_categorical_accuracy: 0.3534 - val_loss: 1.9798 - val_sparse_categorical_accuracy: 0.3501\n",
            "Epoch 3/5\n",
            "469/469 [==============================] - 66s 141ms/step - loss: 1.7920 - sparse_categorical_accuracy: 0.3926 - val_loss: 1.9429 - val_sparse_categorical_accuracy: 0.3127\n",
            "Epoch 4/5\n",
            "469/469 [==============================] - 66s 140ms/step - loss: 1.5887 - sparse_categorical_accuracy: 0.4172 - val_loss: 2.0092 - val_sparse_categorical_accuracy: 0.2807\n",
            "Epoch 5/5\n",
            "469/469 [==============================] - 66s 140ms/step - loss: 1.4386 - sparse_categorical_accuracy: 0.4190 - val_loss: 1.9820 - val_sparse_categorical_accuracy: 0.2540\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As mentioned before, the model is not that accurate, but in next steps we could try to generate new data points by training on the old ones and maybe factoring in also the keywords. \n",
        "\n",
        "*   Train on less categories\n",
        "*   Generate new data per category\n",
        "*   Get more data to generate more points/category from other medical datasets and or wikipedia to get more data.\n",
        "\n"
      ],
      "metadata": {
        "id": "XTUNPYCSEIy6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "oqR8WNND9iez"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_score_and_class_dict = {0: ' Allergy / Immunology', 1: ' Bariatrics', 2: ' Cardiovascular / Pulmonary', 3: ' Neurology', 4: ' Dentistry', 5: ' Urology', 6: ' General Medicine', 7: ' Surgery', 8: ' Speech - Language', 9: ' SOAP / Chart / Progress Notes', 10: ' Sleep Medicine', 11: ' Rheumatology', 12: ' Radiology', 13: ' Psychiatry / Psychology', 14: ' Podiatry', 15: ' Physical Medicine - Rehab', 16: ' Pediatrics - Neonatal', 17: ' Pain Management', 18: ' Orthopedic', 19: ' Ophthalmology', 20: ' Office Notes', 21: ' Obstetrics / Gynecology', 22: ' Neurosurgery', 23: ' Nephrology', 24: ' Letters', 25: ' Lab Medicine - Pathology', 26: ' IME-QME-Work Comp etc.', 27: ' Hospice - Palliative Care', 28: ' Hematology - Oncology', 29: ' Gastroenterology', 30: ' ENT - Otolaryngology', 31: ' Endocrinology', 32: ' Emergency Room Reports', 33: ' Discharge Summary', 34: ' Diets and Nutritions', 35: ' Dermatology', 36: ' Cosmetic / Plastic Surgery', 37: ' Consult - History and Phy.', 38: ' Chiropractic', 39: ' Autopsy'}\n",
        "\n",
        "preds = model(tokenizer([\"14 year old boy, trouble breathing, asthma\"],return_tensors=\"tf\",padding=True,truncation=True))['logits']\n",
        "\n",
        "print(preds)\n",
        "\n",
        "class_preds = np.argmax(preds, axis=1)\n",
        "\n",
        "for pred in class_preds:\n",
        "  print(predict_score_and_class_dict[pred])"
      ],
      "metadata": {
        "id": "XR94rS2t3lmo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbb36571-9e9e-42c8-b97b-9ed02de571fe"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[ 7.8269660e-01  2.0586133e-02 -1.2119799e+00  3.1674702e+00\n",
            "  -2.3513272e+00  3.4068594e+00 -1.2992792e+00 -1.7990235e+00\n",
            "  -4.9186009e-01 -1.8046331e+00  3.2746463e+00  1.1109754e+00\n",
            "   3.4115248e+00  8.1709668e-02  6.3674039e-01  3.0859532e+00\n",
            "  -3.1041843e-03 -2.1184378e+00 -2.1434674e+00 -1.8840517e+00\n",
            "  -2.0328312e+00 -1.0284294e+00 -7.2629625e-01 -1.8761051e+00\n",
            "  -3.6056742e-01  9.8858666e-01 -1.7632463e+00 -1.5262622e+00\n",
            "  -2.9099028e+00  3.1219518e+00 -1.9877642e+00 -1.2213217e+00\n",
            "  -4.8710623e-01 -8.7183535e-01 -1.6263224e+00  4.5898068e-01\n",
            "  -1.3389231e+00 -1.2813189e+00  8.0507624e-01 -1.2063051e+00]], shape=(1, 40), dtype=float32)\n",
            " Radiology\n"
          ]
        }
      ]
    }
  ]
}