Chapter4_Text_Classification.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "Jt5chzAe3_vd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# %%capture\n",
        "!pip install datasets transformers sentence-transformers openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fP6Y4Lct4B6W",
        "outputId": "48e7e364-ab3b-4c85-bedc-e2265712998e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n",
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess<0.70.17 (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
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            "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.10.0\n",
            "    Uninstalling fsspec-2024.10.0:\n",
            "      Successfully uninstalled fsspec-2024.10.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load our data\n",
        "data = load_dataset(\"rotten_tomatoes\")\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610,
          "referenced_widgets": [
            "7780dc0c3a3d408dac72c6b7a0331f8b",
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          ]
        },
        "id": "Vcad44Zk4uER",
        "outputId": "796b270b-d7b4-4d43-b430-c5017f94a756"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/7.46k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7780dc0c3a3d408dac72c6b7a0331f8b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train.parquet:   0%|          | 0.00/699k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed1becbc96394022ad0d089dd304c96a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "validation.parquet:   0%|          | 0.00/90.0k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b18865da4e94e3585115d64f95142a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test.parquet:   0%|          | 0.00/92.2k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4206acd8e33d411daacda7528a4bca14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/8530 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4f4e1d7507774e8db3dd71b91abc2cae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4199f7d96e4e4b5ab9d8db9634286513"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21bafb2a2d0545b4a9b9f44ffd62f3b7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 8530\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 1066\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 1066\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"train\"][0, -1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOdP8h5f8hjU",
        "outputId": "6f521b5b-73c4-46d8-a4b7-bdf4026e75e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': ['the rock is destined to be the 21st century\\'s new \" conan \" and that he\\'s going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .',\n",
              "  'things really get weird , though not particularly scary : the movie is all portent and no content .'],\n",
              " 'label': [1, 0]}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Task Classification with Representation Model"
      ],
      "metadata": {
        "id": "iGe-4RSaDQRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using a task specific model"
      ],
      "metadata": {
        "id": "7Nzw4Sni5tDU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Path to our HF model\n",
        "model_path = \"cardiffnlp/twitter-roberta-base-sentiment-latest\"\n",
        "\n",
        "# Load model into pipeline\n",
        "pipe = pipeline(\n",
        "    model=model_path,\n",
        "    tokenizer=model_path,\n",
        "    return_all_scores=True,\n",
        "    device=\"cuda:0\"\n",
        ")\n"
      ],
      "metadata": {
        "id": "ftfwW9Qa-Igm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340,
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          ]
        },
        "outputId": "88cf2d84-7b15-47ef-9e82-77b280308e1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f26a17b029e84b1d94dec9a9aefad678"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/929 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f187c45528c4473a199bbff6a43cc53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/501M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98e22cc90c1c430cb67b6d579f809f15"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f85f2a8fd0149efa781946bb79e3086"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7396c433c016422881ac21ef523033b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c50fec74bbb44feb4f1fcc91684e67d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/text_classification.py:106: UserWarning: `return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "output = [\n",
        "    {\"label\": \"negative\", \"score\": 0.3},\n",
        "    {\"label\": \"neutral\", \"score\": 0.2},\n",
        "    {\"label\": \"positive\", \"score\": 0.7}\n",
        "]\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "--2IJ9JuT7tc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "from transformers.pipelines.pt_utils import KeyDataset\n",
        "\n",
        "# Run inference\n",
        "y_pred = []\n",
        "for output in tqdm(pipe(KeyDataset(data[\"test\"], \"text\")), total=len(data[\"test\"])):\n",
        "    negative_score = output[0][\"score\"]\n",
        "    positive_score = output[2][\"score\"]\n",
        "    assignment = np.argmax([negative_score, positive_score])\n",
        "    y_pred.append(assignment)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3aiKkLn27KrH",
        "outputId": "48f00555-9e7c-4de6-da83-1014de5a39a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1066/1066 [00:12<00:00, 87.89it/s] \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def evaluate_performance(y_true, y_pred):\n",
        "    \"\"\"Create and print the classification report\"\"\"\n",
        "    performance = classification_report(\n",
        "        y_true, y_pred,\n",
        "        target_names=[\"Negative Review\", \"Positive Review\"]\n",
        "    )\n",
        "    print(performance)\n",
        "\n"
      ],
      "metadata": {
        "id": "Dc-UhIXMVKeJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_performance(data[\"test\"][\"label\"], y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gzoaqHr5Xhz8",
        "outputId": "7808ab43-2014-422f-9997-d88d115c1a46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "Negative Review       0.76      0.88      0.81       533\n",
            "Positive Review       0.86      0.72      0.78       533\n",
            "\n",
            "       accuracy                           0.80      1066\n",
            "      macro avg       0.81      0.80      0.80      1066\n",
            "   weighted avg       0.81      0.80      0.80      1066\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification task that leverage embeddings"
      ],
      "metadata": {
        "id": "QgbUVxHLX7_3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Supervised classification"
      ],
      "metadata": {
        "id": "pIRuAMsfYDHO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# Load model\n",
        "model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\n",
        "\n",
        "# Convert text to embeddings\n",
        "train_embeddings = model.encode(data[\"train\"][\"text\"], show_progress_bar=True)\n",
        "test_embeddings = model.encode(data[\"test\"][\"text\"], show_progress_bar=True)"
      ],
      "metadata": {
        "colab": {
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      "execution_count": null,
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        {
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    },
    {
      "cell_type": "code",
      "source": [
        "train_embeddings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3C166q4lOVH",
        "outputId": "79f43627-7b64-4d29-eafb-0c31f7c7bc80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8530, 768)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Train a Logistic Regression on our train embeddings\n",
        "clf = LogisticRegression(random_state=42)\n",
        "clf.fit(train_embeddings, data[\"train\"][\"label\"])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "MZ1iJsWOlSjb",
        "outputId": "ca79608e-ea6c-4fa7-d196-a3cc8d86d014"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict previously unseen instances\n",
        "y_pred = clf.predict(test_embeddings)\n",
        "evaluate_performance(data[\"test\"][\"label\"], y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RU7dbxaBnirT",
        "outputId": "e15fe134-8c88-450e-aea1-6b158f77b38e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "Negative Review       0.85      0.86      0.85       533\n",
            "Positive Review       0.86      0.85      0.85       533\n",
            "\n",
            "       accuracy                           0.85      1066\n",
            "      macro avg       0.85      0.85      0.85      1066\n",
            "   weighted avg       0.85      0.85      0.85      1066\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Zero Shot Classification"
      ],
      "metadata": {
        "id": "DR0HlG5dv8XW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create embedding for our labels\n",
        "label_embeddings = model.encode(\n",
        "    ['negative_review', 'positive_review'],\n",
        "\n",
        "\n",
        ")\n",
        "print(label_embeddings.shape)\n",
        "print(label_embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsvHVa7OrsnJ",
        "outputId": "3a37208b-2518-43bd-c4f2-f85487ab1189"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 768)\n",
            "[[-0.00399273 -0.07410802  0.01299698 ...  0.01159654  0.00163576\n",
            "   0.01810584]\n",
            " [ 0.00177867 -0.07527933 -0.00126526 ...  0.02073719  0.00125948\n",
            "   0.00521937]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "#find the best matching label for each document\n",
        "print(test_embeddings.shape)\n",
        "print(label_embeddings.shape)\n",
        "sim_matrix = cosine_similarity(test_embeddings, label_embeddings)\n",
        "print(sim_matrix.shape)\n",
        "y_pred = np.argmax(sim_matrix, axis = 1)\n",
        "print(y_pred)\n",
        "print(y_pred.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWmhzEMrGSIt",
        "outputId": "cc91363f-8f67-4017-826b-40ecbdf81e9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1066, 768)\n",
            "(2, 768)\n",
            "(1066, 2)\n",
            "[1 1 0 ... 0 0 0]\n",
            "(1066,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_performance(data[\"test\"][\"label\"], y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDyWhoWOIQ1i",
        "outputId": "e1b9b301-1f41-4d6b-b6e5-61a627c32dcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "Negative Review       0.69      0.93      0.79       533\n",
            "Positive Review       0.89      0.57      0.70       533\n",
            "\n",
            "       accuracy                           0.75      1066\n",
            "      macro avg       0.79      0.75      0.74      1066\n",
            "   weighted avg       0.79      0.75      0.74      1066\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tip!**  \n",
        "\n",
        "What would happen if you were to use different descriptions? Use **\"A very negative movie review\"** and **\"A very positive movie review\"** to see what happens!**Tip!**  \n",
        "\n"
      ],
      "metadata": {
        "id": "V6xlniT6I6p_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification for generative model"
      ],
      "metadata": {
        "id": "2HNwIm1iJAFQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encode Decoder Model"
      ],
      "metadata": {
        "id": "XfKBEfm8LIfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "#load our model\n",
        "pipe = pipeline(\n",
        "    \"text2text-generation\",\n",
        "    model = \"google/flan-t5-base\",\n",
        "    device = \"cuda:0\",\n",
        "\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
            "ab194e57a2174d72a9284664c3150426",
            "6e63c5123da4461989f2b40cd6822cfd",
            "4b519a0fc73347669e348c5ca65a586a",
            "a4bd4c3567ab46118bc94cc0a8e757a5",
            "b90ceb3e4bfe4d6cb733b414d90ed0cb",
            "b80fc069cc2b4729b2625182a9708b32",
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            "b9d6e49965b54a8ebaa7d6e78a844297",
            "ce2804915e0b4ad8971cba904f48b4e7",
            "fa08411d15b447478bdfab20ee4a4485",
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            "a68398121b3f44018b8740a209aed9b3",
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            "1499bb12baa04d0dac6b9f690dd7d5f8",
            "d225bccf1a0a433298960e73d6bf28cc",
            "789ed61333b644ab8a08f0a901e9e46e",
            "b84ee00b9e324ae49d05f3037c338055",
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        "outputId": "6303043a-cafe-4977-9e89-1afad8fe7fce"
      },
      "execution_count": null,
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        {
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            "text/plain": [
              "config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "# prepare our data\n",
        "prompt = \"is the following sentence positive or negative?\"\n",
        "data = data.map(lambda example:{\"t5\":prompt + example[\"text\"]})\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371,
          "referenced_widgets": [
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            "1a42e21540404f8580be19cdd69e147a"
          ]
        },
        "id": "ywixx7OOm7Ax",
        "outputId": "a2dcf23b-f1b7-41e1-b843-54fdd0a3424c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/8530 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "394401e49cd54085b2406c5d71d2396c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acf1ebe357e143f78fb443cce1747aed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1066 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "839422ff95f34a5c8b69bb2845b3fe4b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['text', 'label', 't5'],\n",
            "        num_rows: 8530\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['text', 'label', 't5'],\n",
            "        num_rows: 1066\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['text', 'label', 't5'],\n",
            "        num_rows: 1066\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run inference\n",
        "y_pred = []\n",
        "for output in tqdm(pipe(KeyDataset(data[\"test\"], \"t5\")), total=len(data[\"test\"])):\n",
        "    text = output[0][\"generated_text\"]\n",
        "    y_pred.append(0 if text == \"negative\" else 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1-QrdEJtepd",
        "outputId": "9d6ab971-be63-449a-b38a-0ab7b7f428c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/1066 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1375: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "100%|██████████| 1066/1066 [01:39<00:00, 10.70it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_performance(data[\"test\"][\"label\"], y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WaGbM1_tkJb",
        "outputId": "37e4a8cd-d06f-4012-e12a-1af8fe84725d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 precision    recall  f1-score   support\n",
            "\n",
            "Negative Review       0.86      0.92      0.89       533\n",
            "Positive Review       0.92      0.84      0.88       533\n",
            "\n",
            "       accuracy                           0.88      1066\n",
            "      macro avg       0.89      0.88      0.88      1066\n",
            "   weighted avg       0.89      0.88      0.88      1066\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Chatgpt for classification"
      ],
      "metadata": {
        "id": "wM-V-ZqOtvq1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "# Create client\n",
        "client = openai.OpenAI(api_key=\"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\")"
      ],
      "metadata": {
        "id": "M6JnUhuvxhWs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def chatgpt_generation(prompt, document, model=\"gpt-3.5-turbo-0125\"):\n",
        "    \"\"\"Generate an output based on a prompt and an input document.\"\"\"\n",
        "    messages=[\n",
        "        {\n",
        "            \"role\": \"system\",\n",
        "            \"content\": \"You are a helpful assistant.\"\n",
        "            },\n",
        "        {\n",
        "            \"role\": \"user\",\n",
        "            \"content\":   prompt.replace(\"[DOCUMENT]\", document)\n",
        "            }\n",
        "    ]\n",
        "    chat_completion = client.chat.completions.create(\n",
        "      messages=messages,\n",
        "      model=model,\n",
        "      temperature=0\n",
        "    )\n",
        "    return chat_completion.choices[0].message.content"
      ],
      "metadata": {
        "id": "5jBzvYnuyZeH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a prompt template as a base\n",
        "prompt = \"\"\"Predict whether the following document is a positive or negative movie review:\n",
        "\n",
        "[DOCUMENT]\n",
        "\n",
        "If it is positive return 1 and if it is negative return 0. Do not give any other answers.\n",
        "\"\"\"\n",
        "\n",
        "# Predict the target using GPT\n",
        "document = \"unpretentious , charming , quirky , original\"\n",
        "chatgpt_generation(prompt, document)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "6blIeur_yo1P",
        "outputId": "4b1142c5-9cca-4418-9428-56916bb7fd6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}