31_NLP_su_Huggingface.ipynb

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        "<a href=\"https://colab.research.google.com/github/LeoLin72/IULM_DDM2324_Notebooks/blob/main/31_NLP_su_Huggingface.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZU3rr1SiYKP"
      },
      "source": [
        "# Introduzione a Huggingface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_ZfZFh5yc3yU",
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        "outputId": "685e164d-462e-4288-8cfc-f7e624c03786"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface in /usr/local/lib/python3.10/dist-packages (0.0.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvAeFFyGimWJ"
      },
      "source": [
        "## Importazione delle Librerie"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "HrGbzv4DiA74"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxpHAv2LipG9"
      },
      "source": [
        "## Classificazione del testo\n",
        "\n",
        "La pipeline \"text-classification\" di default opera una sentiment analysis, con due classi: POSITIVE e NEGATIVE, ritornando la classe con probabilitá più elevata.\n",
        "\n",
        "Di default viene utilizzato il modello [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english), che é un fine tuning di BERT([Distilbert](https://huggingface.co/distilbert-base-uncased))"
      ]
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    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
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          "height": 117
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        "id": "o6vlGhP7h8hZ",
        "outputId": "27408a8b-ca08-4c46-d469-990b626aa7eb"
      },
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      label     score\n",
              "0  NEGATIVE  0.999804"
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "\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",
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          "metadata": {},
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      ],
      "source": [
        "classifier = pipeline(\"text-classification\")\n",
        "outputs = classifier(\"This product is awful!\")\n",
        "pd.DataFrame(outputs)"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5WSJU3Xo7xK"
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      "source": [
        "Proviamo la pipeline in italiano:"
      ]
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          "output_type": "execute_result",
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              "      label    score\n",
              "0  POSITIVE  0.56688"
            ],
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              "      fill: #174EA6;\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "                                                    [key], {});\n",
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              "\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",
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              "\n",
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          },
          "metadata": {},
          "execution_count": 23
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      ],
      "source": [
        "outputs = classifier(\"Il prodotto funziona bene\")\n",
        "pd.DataFrame(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1x8Oh39tA2s"
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      "source": [
        "### Test con un modello multilingue\n",
        "\n",
        "Il modello [lxyuan/distilbert-base-multilingual-cased-sentiments-student](https://huggingface.co/lxyuan/distilbert-base-multilingual-cased-sentiments-student) ha avuto un training multilingua"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "0w7WE4BotEg8"
      },
      "outputs": [],
      "source": [
        "\n",
        "classifier = pipeline(\"text-classification\", model=\"lxyuan/distilbert-base-multilingual-cased-sentiments-student\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
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        },
        "id": "1yfPcJoqtETv",
        "outputId": "ef5fe4ff-c1f2-4966-8e1a-a67af9783db8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      label     score\n",
              "0  positive  0.883351"
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              "                                                    [key], {});\n",
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              "        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",
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              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.88335120677948,\n        \"max\": 0.88335120677948,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.88335120677948\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "outputs = classifier(\"Il prodotto funziona bene\")\n",
        "pd.DataFrame(outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "67dDfNlKtWRB",
        "outputId": "713e1ddc-7cbf-40dc-b188-09e58cfda0d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      label     score\n",
              "0  negative  0.800071"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "    .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>label</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>negative</td>\n",
              "      <td>0.800071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-423e3c0b-7b24-46f6-9867-7c9c57e664fb 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-423e3c0b-7b24-46f6-9867-7c9c57e664fb');\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",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8000713586807251,\n        \"max\": 0.8000713586807251,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8000713586807251\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "outputs = classifier(\"Sono veramente soddisfatto\")\n",
        "pd.DataFrame(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XFUZ8ZZrCaT"
      },
      "source": [
        "# Zero-shot classification\n",
        "\n",
        "Si veda, per maggiori dettagli: [https://huggingface.co/tasks/zero-shot-classification](https://huggingface.co/tasks/zero-shot-classification)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "L'output sarà un dizionario con i seguenti campi:\n",
        "\n",
        "**labels:** Le etichette candidate ordinate per probabilità decrescente, basata sulla valutazione del modello su quanto ciascuna etichetta si applichi al testo dato.\n",
        "\n",
        "**scores:** Le probabilità associate a ciascuna etichetta, corrispondenti all'ordine in labels. Questi punteggi rappresentano la confidenza del modello nel classificare il testo nelle rispettive etichette."
      ],
      "metadata": {
        "id": "Ew_OmgrGNCgF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOb-bDjMIZJI",
        "outputId": "4f9fb6b4-2936-4c82-baee-69404f3db77c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'sequence': 'I have a problem with my iphone that needs to be resolved asap!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.5227572917938232, 0.45814111828804016, 0.014264547266066074, 0.002685004845261574, 0.002152057597413659]}\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Inizializzazione della pipeline per la classificazione zero-shot\n",
        "pipe = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")\n",
        "\n",
        "# Esecuzione della classificazione zero-shot\n",
        "result = pipe(\"I have a problem with my iphone that needs to be resolved asap!\",\n",
        "    candidate_labels=[\"urgent\", \"not urgent\", \"phone\", \"tablet\", \"computer\"],\n",
        ")\n",
        "\n",
        "print(result)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
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  "nbformat_minor": 0
}