BERT+Keras.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lz5pZs3GViVF",
        "outputId": "143b5bb2-9f6d-43f4-f967-5d329d12e05d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: tokenizers, flatbuffers, tensorflow-estimator, onnx, keras, tf2onnx, onnxconverter-common, huggingface-hub, transformers, tensorboard, tensorflow-cpu, tensorflow, tensorflow-text, keras-nlp\n",
            "  Attempting uninstall: flatbuffers\n",
            "    Found existing installation: flatbuffers 1.12\n",
            "    Uninstalling flatbuffers-1.12:\n",
            "      Successfully uninstalled flatbuffers-1.12\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.9.0\n",
            "    Uninstalling tensorflow-estimator-2.9.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.9.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.9.0\n",
            "    Uninstalling keras-2.9.0:\n",
            "      Successfully uninstalled keras-2.9.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.9.1\n",
            "    Uninstalling tensorboard-2.9.1:\n",
            "      Successfully uninstalled tensorboard-2.9.1\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.9.2\n",
            "    Uninstalling tensorflow-2.9.2:\n",
            "      Successfully uninstalled tensorflow-2.9.2\n",
            "Successfully installed flatbuffers-2.0.7 huggingface-hub-0.12.0 keras-2.11.0 keras-nlp-0.4.0 onnx-1.12.0 onnxconverter-common-1.13.0 tensorboard-2.11.2 tensorflow-2.11.0 tensorflow-cpu-2.11.0 tensorflow-estimator-2.11.0 tensorflow-text-2.11.0 tf2onnx-1.13.0 tokenizers-0.13.2 transformers-4.26.1\n"
          ]
        }
      ],
      "source": [
        "pip install transformers[tf-cpu]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fxLBMsI33Uj2",
        "outputId": "f4a0c272-916a-462f-e1a1-58f1211a6419"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "pip install --upgrade tensorflow --upgrade-strategy only-if-needed\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRwKOSlnukdB",
        "outputId": "8d68fb6a-e504-4d68-a15f-e17c646f7bd6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (1.0.2)\n",
            "Collecting scikit-learn\n",
            "  Downloading scikit_learn-1.2.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.8/9.8 MB\u001b[0m \u001b[31m73.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (3.1.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.2.0)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.7.3)\n",
            "Installing collected packages: scikit-learn\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
            "    Uninstalling scikit-learn-1.0.2:\n",
            "      Successfully uninstalled scikit-learn-1.0.2\n",
            "Successfully installed scikit-learn-1.2.1\n"
          ]
        }
      ],
      "source": [
        "pip install -U scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C2kqIE4ncG-h",
        "outputId": "848baa0f-b332-4d2e-cc32-8f74c904942a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2022 NVIDIA Corporation\n",
            "Built on Tue_Mar__8_18:18:20_PST_2022\n",
            "Cuda compilation tools, release 11.6, V11.6.124\n",
            "Build cuda_11.6.r11.6/compiler.31057947_0\n"
          ]
        }
      ],
      "source": [
        "!nvcc --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEul-X_ucv3t",
        "outputId": "d9a77258-6126-4dff-8f2d-ffe818387b18"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Thu Feb  9 21:41:01 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   50C    P0    26W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZveJ3YLuwePM",
        "outputId": "46fdffb2-63b6-4ce7-d6f0-20d4648c0bc2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading collection 'all'\n",
            "[nltk_data]    | \n",
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            "[nltk_data]    | Downloading package reuters to /root/nltk_data...\n",
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            "[nltk_data]    | Downloading package rte to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/rte.zip.\n",
            "[nltk_data]    | Downloading package sample_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping grammars/sample_grammars.zip.\n",
            "[nltk_data]    | Downloading package semcor to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package senseval to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/senseval.zip.\n",
            "[nltk_data]    | Downloading package sentence_polarity to\n",
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            "[nltk_data]    |   Unzipping corpora/sentence_polarity.zip.\n",
            "[nltk_data]    | Downloading package sentiwordnet to\n",
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            "[nltk_data]    |   Unzipping corpora/sentiwordnet.zip.\n",
            "[nltk_data]    | Downloading package shakespeare to /root/nltk_data...\n",
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            "[nltk_data]    |   Unzipping grammars/spanish_grammars.zip.\n",
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            "[nltk_data]    |   Unzipping taggers/universal_tagset.zip.\n",
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            "[nltk_data]    |   Unzipping corpora/verbnet.zip.\n",
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            "[nltk_data]    |   Unzipping corpora/webtext.zip.\n",
            "[nltk_data]    | Downloading package wmt15_eval to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping models/wmt15_eval.zip.\n",
            "[nltk_data]    | Downloading package word2vec_sample to\n",
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            "[nltk_data]    |   Unzipping models/word2vec_sample.zip.\n",
            "[nltk_data]    | Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet2021 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet2022 to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet2022.zip.\n",
            "[nltk_data]    | Downloading package wordnet31 to /root/nltk_data...\n",
            "[nltk_data]    | Downloading package wordnet_ic to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/wordnet_ic.zip.\n",
            "[nltk_data]    | Downloading package words to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/words.zip.\n",
            "[nltk_data]    | Downloading package ycoe to /root/nltk_data...\n",
            "[nltk_data]    |   Unzipping corpora/ycoe.zip.\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection all\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import nltk\n",
        "\n",
        "# import all the resources for Natural Language Processing with Python\n",
        "nltk.download(\"all\")\n",
        "import string\n",
        "from nltk import pos_tag\n",
        "from nltk.corpus import wordnet\n",
        "import re\n",
        "from gensim.models import Word2Vec\n",
        "from nltk.tokenize.treebank import TreebankWordDetokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4vq6nIl72-nP"
      },
      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from collections import defaultdict\n",
        "from textwrap import wrap\n",
        "\n",
        "# Torch ML libraries\n",
        "import transformers \n",
        "from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "# Misc.\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "TklAMacsVfYt"
      },
      "outputs": [],
      "source": [
        "from transformers import DistilBertTokenizerFast\n",
        "from transformers import TFDistilBertForSequenceClassification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "8QEqS7D-fZ8x"
      },
      "outputs": [],
      "source": [
        "from preprocessing import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "kO8StlSJWL0W"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        " \n",
        " \n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3SV11oNnWLki",
        "outputId": "632e71e9-5f61-4984-ad43-89b73053cc2c"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "      Unnamed: 0  rating       date   variation  \\\n",
              "3145        3145       5  30-Jul-18  Black  Dot   \n",
              "3146        3146       5  30-Jul-18  Black  Dot   \n",
              "3147        3147       5  30-Jul-18  Black  Dot   \n",
              "3148        3148       5  30-Jul-18  White  Dot   \n",
              "3149        3149       4  29-Jul-18  Black  Dot   \n",
              "\n",
              "                                       verified_reviews  feedback  \n",
              "3145  Perfect for kids, adults and everyone in betwe...         1  \n",
              "3146  Listening to music, searching locations, check...         1  \n",
              "3147  I do love these things, i have them running my...         1  \n",
              "3148  Only complaint I have is that the sound qualit...         1  \n",
              "3149                                               Good         1  "
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import io\n",
        " \n",
        "df = pd.read_csv(io.BytesIO(uploaded[\"amazon_alexa.tsv\"]), sep =\"\\t\")\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 230,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51NrMukmWL-Q",
        "outputId": "dee53a59-5e6d-475d-da5c-0875938ff909"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(2196, 6)\n",
            "(2196, 6)\n",
            "(2196, 6)\n",
            "(2196, 6)\n"
          ]
        }
      ],
      "source": [
        "print(df.shape)\n",
        "df.dropna(inplace = True)\n",
        "print(df.shape)\n",
        "df.drop(df[df.rating == 3].index, inplace=True) #commenti neutrali si eliminano\n",
        "print(df.shape)\n",
        "df.drop_duplicates(subset = \"verified_reviews\", inplace = True)\n",
        "print(df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 231,
      "metadata": {
        "id": "Y_9Uka6wldcG"
      },
      "outputs": [],
      "source": [
        "#Array con reviews che diventano liste a se stanti\n",
        "#Serve x l'undersampling\n",
        "X = np.array(df[\"verified_reviews\"].values).reshape(-1, 1)\n",
        "y = list(df[\"feedback\"].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 232,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cClITYffldm6",
        "outputId": "2239c0df-9598-4312-e664-0198270f1684"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Resampled dataset shape Counter({1: 412, 0: 206})\n"
          ]
        }
      ],
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from collections import Counter\n",
        "\n",
        "#sampling_strategy = 0.5 --> classe di minoranza è il 50% della classe di maggioranza\n",
        "\n",
        "undersampler = RandomUnderSampler(sampling_strategy=0.5, random_state = 0) \n",
        "\n",
        "X, y = undersampler.fit_resample(X, y)\n",
        "\n",
        "\n",
        "print('Resampled dataset shape %s' % Counter(y))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 233,
      "metadata": {
        "id": "rfQ6YuQNldxE"
      },
      "outputs": [],
      "source": [
        "#Riporto allo stato originario - lista di recensioni\n",
        "X_temp = []\n",
        "\n",
        "for rev in X:\n",
        "  X_temp.append(rev[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 234,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8WFTYdMWMB8",
        "outputId": "dd3d53aa-1017-47b8-8bdc-767c306ed8b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total number of types extracted is: 1788\n"
          ]
        }
      ],
      "source": [
        "new_text, new_sent_tok = tokenize_list_of_text(X_temp, custom_stopwords, False, 2)\n",
        "# contengono una lista di tutte le frasi pre processate, nella prima variabile in stringa, nella seconda in tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 235,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asYG1qaIXaxb",
        "outputId": "b570d03c-daa6-4593-cbb7-6031248ef08b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "395 99 124\n"
          ]
        }
      ],
      "source": [
        "#divido in 80% -10%\n",
        "X_train, X_test, y_train, y_test = train_test_split(new_text,y, test_size=0.2, random_state=10)\n",
        "#divido in 80%-20%\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train,y_train, test_size=0.2, random_state=10)\n",
        "\n",
        "print(len(X_train), len(X_val), len(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 236,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "TTBS3TKAWMLq",
        "outputId": "e3f64efe-2c1f-4f7b-a5d7-773f975fa751"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "train = [len(i.split()) for i in X_train]\n",
        "val = [len(i.split()) for i in X_val]\n",
        "test = [len(i.split()) for i in X_test]\n",
        "\n",
        "plt.hist(train, bins=20, color='b', alpha=0.5, label='train')\n",
        "plt.hist(val, bins=20, color='y', alpha=0.5, label='val')\n",
        "plt.hist(test, bins=20, color='r', alpha=0.5, label='test')\n",
        "plt.xlabel('Lunghezza delle recensioni')\n",
        "plt.ylabel('Numero di recensioni')\n",
        "plt.title('Distribuzione della lunghezza delle recensioni')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 237,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tp4hXhUsYzKI",
        "outputId": "33c52566-09ae-46e4-ca03-cfaa52e2987f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "134\n"
          ]
        }
      ],
      "source": [
        "#Mi calcolo la lunghezza massima delle recensioni\n",
        "def longest_string(list_of_strings):\n",
        "    max_words = 0\n",
        "    for string in list_of_strings:\n",
        "        list_words = string.split()\n",
        "        if len(list_words) > max_words:\n",
        "            max_words = len(list_words)\n",
        "    return max_words\n",
        "\n",
        "max_length = longest_string(new_text)\n",
        "print(max_length)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 238,
      "metadata": {
        "id": "m92-9pZsLZoe"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tokenizers import BertWordPieceTokenizer\n",
        "from transformers import BertTokenizer, TFBertModel, BertConfig\n",
        "#Tokenizzatore\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 239,
      "metadata": {
        "id": "8szPXW1mY2OF"
      },
      "outputs": [],
      "source": [
        "def prepare_data(sentences):\n",
        "  #creo le due liste che conterranno gli ids e le attention mask  \n",
        "  input_ids_list = []\n",
        "  attention_masks_list = []\n",
        "\n",
        "  for sentence in sentences:\n",
        "    encoded_dict = tokenizer.encode_plus(sentence, max_length=max_length,add_special_tokens = True, pad_to_max_length=True, return_attention_mask=True)\n",
        "    \n",
        "    #converto in tensori ogni ids e attention mask di ogni recensione e li appendo alle liste\n",
        "    input_ids = tf.convert_to_tensor(encoded_dict['input_ids'])\n",
        "    attention_masks = tf.convert_to_tensor(encoded_dict['attention_mask'])\n",
        "\n",
        "    input_ids_list.append(input_ids)\n",
        "    attention_masks_list.append(attention_masks)\n",
        "\n",
        "  #La funzione stack serve a concatenare i tensori contenuti in input_ids_list e attention_masks_list lungo un nuovo asse, \n",
        "  #creando così un tensore più grande che rappresenta tutti i tensori singoli concatenati.\n",
        "  #Senza avrei una lista di tensori singoli, mentre il modello richiede tensori di forma uniforme come input.\n",
        "  input_ids = tf.stack(input_ids_list)\n",
        "  attention_masks = tf.stack(attention_masks_list)\n",
        "\n",
        "  return input_ids, attention_masks\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 240,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9m32HmTcY5CE",
        "outputId": "e47b0722-42ab-4cea-84e5-489e5d668ba7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"
          ]
        }
      ],
      "source": [
        "train_input_ids, train_masks = prepare_data(X_train)\n",
        "val_input_ids, val_masks = prepare_data(X_val)\n",
        "test_input_ids, test_masks = prepare_data(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 241,
      "metadata": {
        "id": "LZxNrCE1uMq2"
      },
      "outputs": [],
      "source": [
        "#Trasformo in tensori anche le label, altrimenti il modello nell'addestramento da errore\n",
        "y_train = tf.convert_to_tensor(y_train, dtype=tf.float32)\n",
        "y_val = tf.convert_to_tensor(y_val, dtype=tf.float32)\n",
        "y_test = tf.convert_to_tensor(y_test, dtype=tf.float32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 242,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EiWyPoEZ9KU",
        "outputId": "8e941c13-109e-4148-819d-64455e8a2645"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(395, 134)\n",
            "(395, 134)\n",
            "(395,)\n"
          ]
        }
      ],
      "source": [
        "print(train_input_ids.shape)\n",
        "print(train_masks.shape)\n",
        "print(y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 243,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSyJgDuy7OGl",
        "outputId": "acdc2272-c7f2-45ce-bae8-3e6adb3b25f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original:  love\n",
            "Token IDs: tf.Tensor(\n",
            "[ 101 1567  102    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0], shape=(134,), dtype=int32)\n",
            "Attention Mask: tf.Tensor(\n",
            "[1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0], shape=(134,), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "# Print sentence 0, now as a list of IDs.\n",
        "print('Original: ', X_train[0])\n",
        "print('Token IDs:', train_input_ids[0])\n",
        "print('Attention Mask:', train_masks[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 244,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_saXPbw37lJN",
        "outputId": "2813a4be-0ddb-46ae-ba5e-f765f5447a81"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[CLS] love [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n"
          ]
        }
      ],
      "source": [
        "if len(train_input_ids.shape) > 1:\n",
        "    data= train_input_ids[0]\n",
        "\n",
        "print(tokenizer.decode(data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 245,
      "metadata": {
        "id": "TBeNaEETR7oH"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score, recall_score, accuracy_score\n",
        "from transformers import BertTokenizer, TFBertForSequenceClassification\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 246,
      "metadata": {
        "id": "bZ88N8f8Kb_Y"
      },
      "outputs": [],
      "source": [
        "def create_model():\n",
        "    # BERT encoder\n",
        "    encoder = TFBertForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=2)\n",
        "    \n",
        "    # # Unfreeze the BERT layers for fine-tuning\n",
        "    # encoder.trainable = False\n",
        "\n",
        "    input_ids = layers.Input(shape=(max_length,), dtype=tf.int32)\n",
        "    attention_mask = layers.Input(shape=(max_length,), dtype=tf.int32)\n",
        "    \n",
        "    #il modello BERT restituisce 2 oggetti, il primo che è la rappresentazione vettoriale del testo di input,\n",
        "    #il secondo che rappresenta il risultato della pooling sull aprima colonna della matrice di attenzione\n",
        "    embedding = encoder(\n",
        "        input_ids, attention_mask=attention_mask\n",
        "    )[0]\n",
        "\n",
        "    dropout = layers.Dropout(0.2)(embedding)\n",
        "\n",
        "    dense_layer = Dense(128, activation='relu')(dropout)\n",
        "    \n",
        "    #Il tasso di dropout del 20% è un valore di default comunemente utilizzato,\n",
        "    # ma può essere regolato per ottenere il miglior equilibrio tra overfitting e generalizzazione del modello. \n",
        "    #Il tasso di dropout specifica la quantità di neuroni che verranno casualmente disattivati durante la formazione del modello\n",
        "    dropout = layers.Dropout(0.2)(dense_layer)\n",
        "    #I dati di input vengono trasformati in embedding  e passati a un layer \"dense\" con funzione di attivazione sigmoid\n",
        "    class_prob = layers.Dense(1, activation='sigmoid')(dropout)\n",
        "    class_prob = layers.Flatten()(class_prob)\n",
        "\n",
        "\n",
        "    model = keras.Model(\n",
        "        inputs=[input_ids, attention_mask],\n",
        "        outputs=class_prob,\n",
        "    )\n",
        "\n",
        "    #la funzione di perdita - binary cross-entropy\n",
        "    loss = keras.losses.BinaryCrossentropy(from_logits=False)\n",
        "    #ottimizzatore Adam\n",
        "    optimizer = keras.optimizers.Adam(learning_rate=2e-5)\n",
        "    \n",
        "    model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 254,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJZ4exR4K8-g",
        "outputId": "97b82cfb-e1ac-4ded-999c-b7f3b3b42ab0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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-cased 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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_17\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_35 (InputLayer)          [(None, 134)]        0           []                               \n",
            "                                                                                                  \n",
            " input_36 (InputLayer)          [(None, 134)]        0           []                               \n",
            "                                                                                                  \n",
            " tf_bert_for_sequence_classific  TFSequenceClassifie  108311810  ['input_35[0][0]',               \n",
            " ation_17 (TFBertForSequenceCla  rOutput(loss=None,               'input_36[0][0]']               \n",
            " ssification)                   logits=(None, 2),                                                 \n",
            "                                 hidden_states=None                                               \n",
            "                                , attentions=None)                                                \n",
            "                                                                                                  \n",
            " dropout_718 (Dropout)          (None, 2)            0           ['tf_bert_for_sequence_classifica\n",
            "                                                                 tion_17[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_34 (Dense)               (None, 128)          384         ['dropout_718[0][0]']            \n",
            "                                                                                                  \n",
            " dropout_719 (Dropout)          (None, 128)          0           ['dense_34[0][0]']               \n",
            "                                                                                                  \n",
            " dense_35 (Dense)               (None, 1)            129         ['dropout_719[0][0]']            \n",
            "                                                                                                  \n",
            " flatten_17 (Flatten)           (None, 1)            0           ['dense_35[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 108,312,323\n",
            "Trainable params: 108,312,323\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = create_model()\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 262,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xT-iYJRhf5sQ",
        "outputId": "948cf7b6-af77-4f92-9c72-0a4c3d4af807"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/8\n",
            "25/25 [==============================] - 12s 484ms/step - loss: 0.5133 - accuracy: 0.7494 - val_loss: 0.4821 - val_accuracy: 0.8586\n",
            "Epoch 2/8\n",
            "25/25 [==============================] - 12s 474ms/step - loss: 0.4072 - accuracy: 0.8152 - val_loss: 0.4418 - val_accuracy: 0.8485\n",
            "Epoch 3/8\n",
            "25/25 [==============================] - 12s 470ms/step - loss: 0.3430 - accuracy: 0.8481 - val_loss: 0.4011 - val_accuracy: 0.8788\n",
            "Epoch 4/8\n",
            "25/25 [==============================] - 12s 474ms/step - loss: 0.3312 - accuracy: 0.8886 - val_loss: 0.3990 - val_accuracy: 0.8687\n",
            "Epoch 5/8\n",
            "25/25 [==============================] - 12s 475ms/step - loss: 0.2949 - accuracy: 0.9114 - val_loss: 0.3997 - val_accuracy: 0.8485\n",
            "Epoch 6/8\n",
            "25/25 [==============================] - 12s 476ms/step - loss: 0.2603 - accuracy: 0.9013 - val_loss: 0.3628 - val_accuracy: 0.8889\n",
            "Epoch 7/8\n",
            "25/25 [==============================] - 12s 466ms/step - loss: 0.2429 - accuracy: 0.8987 - val_loss: 0.3816 - val_accuracy: 0.8889\n",
            "Epoch 8/8\n",
            "25/25 [==============================] - 12s 470ms/step - loss: 0.2141 - accuracy: 0.9063 - val_loss: 0.4025 - val_accuracy: 0.8687\n"
          ]
        }
      ],
      "source": [
        "# Addestramento del modello\n",
        "training = model.fit([train_input_ids, train_masks], y_train, epochs=8,batch_size=16, validation_data=([val_input_ids, val_masks], y_val))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOtYnqJEtpCB"
      },
      "source": [
        "L'overfitting si verifica quando il modello ha una bassa perdita (loss) sul set di allenamento, ma una più alta perdita sulla valutazione. In questo caso, i valori di perdita e precisione (accuracy) sul set di valutazione sono relativamente costanti durante i diversi epoch, il che suggerisce che il modello non sta overfitting.    \n",
        "    \"loss\" indica la funzione di perdita, ovvero la misura della differenza tra le previsioni del modello e i valori effettivi. Una perdita più bassa indica che il modello sta facendo previsioni più accurate.\n",
        "    \"accuracy\" è la precisione, ovvero la percentuale di previsioni corrette effettuate dal modello.\n",
        "    \"val_loss\" e \"val_accuracy\" sono rispettivamente la perdita e la precisione calcolate sul set di dati di convalida, che vengono utilizzati per valutare la generalizzabilità del modello."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 263,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eL8mYNAEfgI2",
        "outputId": "c0230aed-7eee-43f9-d735-4f23fa48c819"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 1s 206ms/step - loss: 0.4025 - accuracy: 0.8687\n"
          ]
        }
      ],
      "source": [
        "# Valutazione del modello\n",
        "results = model.evaluate([val_input_ids, val_masks], y_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 264,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jsir-MlPK9Be",
        "outputId": "9b7d8ba2-440e-4243-ad27-2fceed75d927"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 1s 277ms/step - loss: 0.3787 - accuracy: 0.8710\n",
            "Test Loss: 0.3787 Test Accuracy: 0.8710\n"
          ]
        }
      ],
      "source": [
        "risultati = model.evaluate([test_input_ids, test_masks], y_test)\n",
        "loss, accuracy = risultati[0], risultati[1]\n",
        "print(\"Test Loss: {:.4f} Test Accuracy: {:.4f}\".format(loss, accuracy))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 265,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaVgjz5fK9Eb",
        "outputId": "af87d665-79c6-4362-acc4-24df45e12c0b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 1s 275ms/step\n",
            "Accuracy:  0.8709677419354839\n"
          ]
        }
      ],
      "source": [
        "# Eseguire le previsioni sul set di test\n",
        "predictions = model.predict([test_input_ids, test_masks])\n",
        "\n",
        "# Convertire le previsioni in classi binarie (0 o 1) utilizzando una soglia di 0,5\n",
        "predictions_classes = (predictions > 0.5).astype(int)\n",
        "\n",
        "# Valutare le prestazioni del modello\n",
        "accuracy = accuracy_score(y_test, predictions_classes)\n",
        "print(\"Accuracy: \", accuracy)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 266,
      "metadata": {
        "id": "j6rJ08zx08GY"
      },
      "outputs": [],
      "source": [
        "predictions = np.round(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YjorXuP8cUD",
        "outputId": "8d0b38e7-a16a-40be-e91d-5a8cf1aefdac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.83      0.93      0.87        41\n",
            "         1.0       0.96      0.90      0.93        83\n",
            "\n",
            "    accuracy                           0.91       124\n",
            "   macro avg       0.89      0.92      0.90       124\n",
            "weighted avg       0.92      0.91      0.91       124\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, predictions))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "43973268aa23c7cf4b4cafe0e819fac9b2412fcc991a21988492c3271a4e3f9b"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}