Notebooks/02_AITEXTIFICATION_Embeddings.ipynb

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      "source": [
        "# 📚 PROYECTO AI-TEXTIFICATION\n",
        "## 👅 Procesamiento de Lenguaje Natural\n",
        "## 💻 Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas\n",
        "## 🏫 Universidad Nacional Autónoma de México\n",
        "\n",
        "<hr>\n",
        "\n",
        "### 🤖 AI-TEXTIFICATION\n",
        "### 📓 CUADERNO [02]: EMBEDDINGS\n",
        "### 📄 Detección de autoría en textos AI - Humanos:\n",
        "\n",
        "#### 🔵 **Tarea A**: Clasificación Binaria:\n",
        "1. Texto de Humano.\n",
        "2. Texto de Inteligencia Artificial.\n",
        "\n",
        "#### 🔵 **Tarea B**: Clasificación Multiclase:\n",
        "1. Texto de ChatGPT.\n",
        "2. Texto de Cohere.\n",
        "3. Texto de Davinci.\n",
        "4. Texto de Dolly.\n",
        "5. Texto de Humano.\n",
        "\n",
        "**👬 Autores:**\n",
        "* León Rosas Manuel Alejandro.\n",
        "* Ramos Herrera Iván Alejandro.\n"
      ],
      "metadata": {
        "id": "P7F5VNFJMckc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [01] 🎯 Objetivo\n",
        "\n",
        "**EN ESTE NOTEBOOK SE OBTENDRÁ EL DATASET LIMPIADOS Y SE LES APLICARÁN EMBEDDINGS [PROPIOS Y DE LA LIBRERÍA GENSIM] Y SE GUARDARÁN**.\n",
        "\n",
        "**DATASETS:**\n",
        "1. **PA: Cleaned => Lemma => UNK => GENSIM Embbedings**.\n",
        "2. **PB: Cleaned => Lemma => UNK => OWN Embbedings**.\n",
        "3. **PC: Cleaned => UNK => GENSIM Embbedings**.\n",
        "4. **PD: Cleaned => UNK => OWN Embbeds**."
      ],
      "metadata": {
        "id": "-TQwHu8N3nzR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [02] 📚 Datasets"
      ],
      "metadata": {
        "id": "8hffjg5vJPVU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Obtención"
      ],
      "metadata": {
        "id": "oMogqegiJXqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download(\"punkt\")\n",
        "from utils import sigmoid, get_batches, compute_pca, get_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EBUX7PyjJaJZ",
        "outputId": "91f0ee75-150a-49a1-ff1f-faa2bcc128c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [03] 🔳 Embeddings [A]"
      ],
      "metadata": {
        "id": "FfIxquQmnMBW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset AB:\n",
        "AB_train = pd.read_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskA-TrainAB.csv\")\n",
        "AB_test = pd.read_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskA-DevAB.csv\")\n",
        "\n",
        "# Dataset CD:\n",
        "CD_train = pd.read_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskA-TrainCD.csv\")\n",
        "CD_test = pd.read_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskA-DevCD.csv\")"
      ],
      "metadata": {
        "id": "ZHAt6r5WndYF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementaciones"
      ],
      "metadata": {
        "id": "9rNtc4OUrlXi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### OWN EMBEDDINGS"
      ],
      "metadata": {
        "id": "vd5uCgHe4oI_"
      }
    },
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      "cell_type": "code",
      "source": [
        "class Embeddings:\n",
        "\n",
        "  # Constructor:\n",
        "  def __init__(self, vocabulary_size: int, dimension: int):\n",
        "    \"\"\"\n",
        "      ATTRIBUTES:\n",
        "        - vocabulary_size [int]: Size of the vocabulary.\n",
        "        - dimension [int]: Dimension of the hoped embedding.\n",
        "    \"\"\"\n",
        "    self.V = vocabulary_size\n",
        "    self.D = dimension\n",
        "\n",
        "    # Initializate the Weights and Biases of the FFD NN for CBOW:\n",
        "    # XAVIER INITIALIZATION:\n",
        "    np.random.seed(11)\n",
        "    self.W1 = np.random.randn(self.D, self.V) * np.sqrt(1 / self.V)\n",
        "    self.W2 = np.random.randn(self.V, self.D) * np.sqrt(1 / self.D)\n",
        "    self.b1 = np.zeros((self.D, 1))\n",
        "    self.b2 = np.zeros((self.V, 1))\n",
        "    self.grad_W1 = self.grad_W2 = self.grad_b1 = self.grad_b2 = None\n",
        "    assert self.W1.shape == ((self.D, self.V))\n",
        "    assert self.W2.shape == ((self.V, self.D))\n",
        "\n",
        "\n",
        "  # Activation function:\n",
        "  def softmax(self, x):\n",
        "    \"\"\"Returns the computation for Softmax(x), to the output layer\"\"\"\n",
        "    exp_x = np.exp(x - np.max(x))\n",
        "    return exp_x / np.sum(exp_x, axis=0, keepdims=True)\n",
        "\n",
        "\n",
        "  # FEEDFORWARD NEURAL NETWORK FUNCTIONS:\n",
        "  def cost(self, y_real, y_predicted, batch_size):\n",
        "    \"\"\"Computes the Cross Entropy function(y_real, y_predicted, batch_size)\"\"\"\n",
        "    logprobs = np.multiply(np.log(y_predicted),y_real) + np.multiply(np.log(1 - y_predicted), 1 - y_real)\n",
        "    cost = - 1/batch_size * np.sum(logprobs)\n",
        "    cost = np.squeeze(cost)\n",
        "    return cost\n",
        "\n",
        "\n",
        "  def forward(self, x):\n",
        "    \"\"\"Computes the Forward step on the FFD NN as Wx + b across the layer.\"\"\"\n",
        "    \"\"\"[h1 = ReLU(W1 * x + b1)] => [a2 = (W2 * h1 + b2)]\"\"\"\n",
        "\n",
        "    # Preactivation a1:\n",
        "    a1 = self.W1 @ x + self.b1\n",
        "    # h1 = ReLU(a1):\n",
        "    h1 = np.maximum(0, a1)\n",
        "    # Preactivation a2:\n",
        "    a2 = self.W2 @ h1 + self.b2\n",
        "    return a2, h1\n",
        "\n",
        "\n",
        "  def backward(self, x, y_predicted, y_real, h, batch_size):\n",
        "    \"\"\"Computes Backrpopagation across Ouput Layer => a1\"\"\"\n",
        "    # Layer 1:\n",
        "    l1 = self.W2.T @ (y_predicted - y_real)\n",
        "    # ReLu:\n",
        "    l1 = np.maximum(0, l1)\n",
        "\n",
        "    # Gradients:\n",
        "    self.grad_W1 = (1 / batch_size) * np.dot(l1, x.T)\n",
        "    self.grad_W2 = (1 / batch_size) * np.dot(y_predicted - y_real, h.T)\n",
        "    self.grad_b1 = (1 / batch_size) * np.sum(l1, axis=1, keepdims=True)\n",
        "    self.grad_b2 = (1 / batch_size) * np.sum(y_predicted - y_real, axis=1, keepdims=True)\n",
        "\n",
        "\n",
        "  def embeddings(self, text, wordIndexes, iterations, learning_rate=0.001, batch_size=20):\n",
        "    \"\"\"Optimizator to update Weights and Biases using Gradient Descend and returns the embeddings (Weights)\"\"\"\n",
        "    for x, y in get_batches(text, wordIndexes, self.V, 2, batch_size):\n",
        "      # Forward Step:\n",
        "      a2, h1 = self.forward(x)\n",
        "      # Prediction:\n",
        "      y_predicted = self.softmax(a2)\n",
        "      # Cost:\n",
        "      cost = -np.sum(y * np.log(y_predicted)) / batch_size\n",
        "      # if ((iterations+1) % 10 == 0):\n",
        "      #  print(f\"iterations: {iterations + 1} cost: {cost:.6f}\")\n",
        "      # Backpropagation step:\n",
        "      self.backward(x, y_predicted, y, h1, batch_size)\n",
        "\n",
        "      # Updating the Weights and Biases:\n",
        "      self.W1 -= learning_rate * self.grad_W1\n",
        "      self.W2 -= learning_rate * self.grad_W2\n",
        "      self.b1 -= learning_rate * self.grad_b1\n",
        "      self.b2 -= learning_rate * self.grad_b2\n",
        "\n",
        "      iterations += 1\n",
        "      if iterations == iterations:\n",
        "          break\n",
        "      if iterations % 100 == 0:\n",
        "          learning_rate *= 0.66\n",
        "\n",
        "    # Returns the embeddings:\n",
        "    return (self.W1.T + self.W2) / 2.0\n",
        "\n",
        "\n",
        "  def get_embedding_of(self, word, word2Index):\n",
        "    \"\"\"\n",
        "    Obtiene el embedding para una palabra específica.\n",
        "\n",
        "    Parameters:\n",
        "    - word (str): La palabra para la cual se desea obtener el embedding.\n",
        "    - word2Index (dict): Diccionario que mapea palabras a índices.\n",
        "\n",
        "    Returns:\n",
        "    - np.ndarray: El embedding correspondiente a la palabra.\n",
        "    \"\"\"\n",
        "    if word in word2Index:\n",
        "      word_index = word2Index[word]\n",
        "      print(f\"Índice de la palabra \\\"{word}\\\": {word_index}\")\n",
        "\n",
        "      embedding = (self.W1.T[word_index] + self.W2[word_index]) / 2.0\n",
        "      print(f\"Embedding calculado: {embedding}\")\n",
        "\n",
        "      return embedding\n",
        "    else:\n",
        "      print(f\"La palabra \\\"{word}\\\" no está en el vocabulario.\")\n",
        "      print(word)\n",
        "      return np.zeros(self.D)\n",
        "\n",
        "\n",
        "  def view_embeddings(self, words, word2Index):\n",
        "    from matplotlib import pyplot\n",
        "    valid_indices = [word2Index[word] for word in words if word in word2Index]\n",
        "\n",
        "    X = embedsitos[valid_indices, :]\n",
        "    print(X.shape, valid_indices)\n",
        "\n",
        "    result = compute_pca(X, 2)\n",
        "\n",
        "    pyplot.scatter(result[:, 0], result[:, 1])\n",
        "\n",
        "    for i, word in enumerate(words):\n",
        "        if word in word2Index:\n",
        "            idx = valid_indices.index(word2Index[word])\n",
        "            pyplot.annotate(word, xy=(result[idx, 0], result[idx, 1]))\n",
        "\n",
        "    pyplot.show()"
      ],
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      "source": [
        "def own_embeddings(dataframe, text_column=\"text\", dimensions=50, iterations=1000, learning_rate=0.001, batch_size=1):\n",
        "  # Obtiene el diccionario de palabras:\n",
        "  whole_text = \" \".join(dataframe[text_column])\n",
        "  tokens = word_tokenize(whole_text)\n",
        "  tokens.append(\" \")\n",
        "  word2Index, _ = get_dict(tokens)\n",
        "  print(\"Tamaño del vocabulario:\", len(word2Index))\n",
        "\n",
        "  # Inicializa el objeto de embeddings:\n",
        "  EMB = Embeddings(vocabulary_size=len(word2Index), dimension=dimensions)\n",
        "\n",
        "  # Obtiene embeddings para toda la columna en el DataFrame:\n",
        "  embeddings = EMB.embeddings(whole_text, word2Index, iterations, learning_rate)\n",
        "\n",
        "  return embeddings, word2Index, EMB"
      ],
      "metadata": {
        "id": "ZN1pM_cTwxIA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ejemplo de uso"
      ],
      "metadata": {
        "id": "uNupZAxVA2GR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embedsitos, word2Index, emb = own_embeddings(AB_test, iterations=2000, learning_rate=0.0001)\n",
        "print(embedsitos.shape)\n",
        "emb.get_embedding_of(\"computer\", word2Index)\n",
        "view_words = [\"computer\", \"king\", \"queen\", \"kingdom\", \"happy\", \"sad\", \"dog\", \"run\", \"science\", \"lolxd\", \"unk\"]\n",
        "emb.view_embeddings(view_words, word2Index)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "LpEgV9EMt8Jy",
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          "text": [
            "Tamaño del vocabulario: 18887\n",
            "(18887, 50)\n",
            "Índice de la palabra \"computer\": 4177\n",
            "Embedding calculado: [ 0.045846   -0.05224661  0.09487325  0.06001613  0.01304955 -0.0674416\n",
            " -0.04988113 -0.06065706 -0.04037847 -0.04839922  0.05238548  0.07383298\n",
            " -0.02626773 -0.00044722 -0.05665265 -0.10181015 -0.0797675   0.07552308\n",
            "  0.02023089 -0.0915984   0.00552453  0.01035424  0.01675895 -0.01054961\n",
            "  0.011903   -0.04685713  0.03256091 -0.06374772 -0.00129824 -0.01877435\n",
            "  0.01408439 -0.10200335  0.03171838  0.0381566  -0.01531156 -0.06592213\n",
            " -0.04305332 -0.00167716  0.05010012  0.01306855 -0.07377424 -0.03814982\n",
            "  0.04244974 -0.16680745 -0.00260824 -0.02935439 -0.00689167  0.05153336\n",
            " -0.02121076  0.0084323 ]\n",
            "(10, 50) [4177, 9802, 13683, 9803, 8123, 14822, 5693, 14773, 15024, 17785]\n"
          ]
        },
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### PYTHON LIBRARY EMBEDDINGS"
      ],
      "metadata": {
        "id": "LFZpoPPVGrGj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gensim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1C_8Ru6G0fW",
        "outputId": "018a7970-2680-42e1-ad94-dd231b8c07cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.11.3)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (6.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import Word2Vec\n",
        "from nltk.tokenize import word_tokenize"
      ],
      "metadata": {
        "id": "BYhpTu0TD7ia"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize the sentences:\n",
        "tokenized_sentences = [word_tokenize(sentence.lower()) for sentence in lines]\n",
        "\n",
        "# Train Word2Vec model:\n",
        "model = Word2Vec(sentences=tokenized_sentences, vector_size=50, window=5, min_count=1, workers=4)\n",
        "\n",
        "# Save the model:\n",
        "model.save(\"word2vec.model\")\n",
        "\n",
        "# Get the word vector for a specific word:\n",
        "word_vector = model.wv[\"little\"]\n",
        "print(f\"Vector for \\\"little\\\": {word_vector}\")\n",
        "\n",
        "# Similarity between two words:\n",
        "similarity = model.wv.similarity(\"kingdom\", \"king\")\n",
        "print(f\"Similarity between \\\"kingdom\\\" and \\\"king\\\": {similarity}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7xUFZ-fGx7g",
        "outputId": "44bc4e21-146a-4dfd-f15e-1e58fb73b081"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vector for \"little\": [-0.00635842 -0.06396496  0.00608176  0.07513064 -0.10107178 -0.1780544\n",
            "  0.14216593  0.33434093 -0.24632278 -0.16182567  0.04969298 -0.21819204\n",
            "  0.03386923  0.22442748 -0.13066857  0.12986629  0.15950257  0.00782079\n",
            " -0.36780035 -0.1717765   0.01976203  0.20504047  0.3643737   0.00560989\n",
            "  0.04927661  0.04978066  0.00109018 -0.00640525 -0.11617131  0.03706574\n",
            "  0.1437774  -0.10265845 -0.0465221  -0.16134077 -0.04047582  0.09819089\n",
            "  0.2401363   0.01229743  0.14250459 -0.10995676  0.24078216 -0.04479929\n",
            " -0.0719698   0.00566156  0.41558087  0.11065891 -0.08807793  0.01718168\n",
            "  0.13502286  0.04408467]\n",
            "Similarity between \"kingdom\" and \"king\": 0.9445829391479492\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def gensim_embeddings(dataframe, text_column=\"text\", dimensions=50, window=5, min_count=1, workers=4):\n",
        "  # Tokeniza el texto:\n",
        "  tokenized_sentences = [word_tokenize(sentence.lower()) for sentence in dataframe[text_column]]\n",
        "\n",
        "  # Entrenar el modelo Word2Vec:\n",
        "  model = Word2Vec(sentences=tokenized_sentences, vector_size=dimensions, window=window, min_count=min_count, workers=workers)\n",
        "\n",
        "  # Obtener embeddings para cada texto en el DataFrame:\n",
        "  dataframe[\"embeddings\"] = dataframe[text_column].apply(lambda text: get_embeddings(text, model))\n",
        "\n",
        "  return dataframe, model\n",
        "\n",
        "\n",
        "def get_embeddings(text, model):\n",
        "  # Tokeniza y procesa el texto:\n",
        "  tokens = word_tokenize(text.lower())\n",
        "\n",
        "  # Obtiene el vector promedio de embeddings de palabras:\n",
        "  embeddings = [model.wv[token] for token in tokens if token in model.wv]\n",
        "  if not embeddings:\n",
        "    # Si no hay embeddings, retornar un vector de ceros:\n",
        "    return np.zeros(model.vector_size)\n",
        "  return np.mean(embeddings, axis=0)\n",
        "\n",
        "\n",
        "def view_gensim(words, model):\n",
        "  valid_words = [word for word in words if word in model.wv]\n",
        "\n",
        "  # Obtiene los embeddings para las palabras válidas:\n",
        "  X = [model.wv[word] for word in valid_words]\n",
        "  X = np.array(X)\n",
        "\n",
        "  print(X.shape, valid_words)\n",
        "\n",
        "  # Aplica PCA a los embeddings:\n",
        "  pca = PCA(n_components=2)\n",
        "  result = pca.fit_transform(X)\n",
        "\n",
        "  # Visualiza los embeddings:\n",
        "  plt.scatter(result[:, 0], result[:, 1])\n",
        "  for i, word in enumerate(valid_words):\n",
        "    idx = words.index(word)\n",
        "    plt.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "Od8X961XjBg3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ejemplo de uso"
      ],
      "metadata": {
        "id": "fPo5TiUjEWxS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_gensim, model_gensim = gensim_embeddings(AB_test, dimensions=50, window=5, min_count=1, workers=4)\n",
        "print(df_gensim[\"embeddings\"].shape)\n",
        "print(\"Vector para \\\"computer\\\":\\n\", model_gensim.wv[\"computer\"])\n",
        "\n",
        "view_words = [\"computer\", \"king\", \"queen\", \"kingdom\", \"happy\", \"sad\", \"dog\", \"run\", \"science\", \"lolxd\", \"unk\"]\n",
        "view_gensim(view_words, model_gensim)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "vmwQw-EPEZ5J",
        "outputId": "56a1de1e-7dbb-4ac4-a147-3e79b999cc0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5000,)\n",
            "Vector para \"computer\":\n",
            " [ 0.01872814  0.61278784 -0.8637215   0.6796528  -1.9468354  -1.0770389\n",
            "  0.79480714  0.4223309  -2.151323    0.47885495  0.05581171 -1.134505\n",
            "  0.68822086  1.0381188   2.0538232  -0.99861145 -0.2788916  -0.9172653\n",
            "  0.48028585 -1.6959043  -0.1866316  -0.41082233  0.77889043  0.28801018\n",
            "  0.9204819  -0.6734427   0.8454199  -0.36540842  1.1906438  -0.8396483\n",
            " -0.30666092  0.55653465  0.45937142  0.9032368  -0.4742202  -1.2499065\n",
            " -0.38450286  1.1851611  -0.7325303  -0.8080676   0.89294076  0.05001917\n",
            " -0.02660507 -0.03860467  1.3522491   1.244368   -0.26224878 -0.7421615\n",
            "  0.3430547   1.0515698 ]\n",
            "(10, 50) ['computer', 'king', 'queen', 'kingdom', 'happy', 'sad', 'dog', 'run', 'science', 'unk']\n"
          ]
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Embeddings de Datasets"
      ],
      "metadata": {
        "id": "L0EWg8d0LXoJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [A]"
      ],
      "metadata": {
        "id": "PBa_fbOGLiXI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embeddings A: Dataset AB => Embeddings Gensim:\n",
        "A_gensim_df, A_gensim_model = gensim_embeddings(AB_train, dimensions=50, window=5, min_count=1, workers=4)"
      ],
      "metadata": {
        "id": "fodSRpW6Lrjj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "A_gensim_model.save(\"/content/drive/MyDrive/Datasets/AITextification/A_gensim_model.bin\")\n",
        "A_gensim_df[\"embeddings\"].to_csv(\"/content/drive/MyDrive/Datasets/AITextification/A_gensim_embeddings.csv\", index=False)"
      ],
      "metadata": {
        "id": "cq55yS0qNbmv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [B]"
      ],
      "metadata": {
        "id": "uh7vO6iVLvmw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embeddings B: Dataset AB => Embeddings Propios:\n",
        "B_embeds, B_word2Index, B_embeds_model = own_embeddings(AB_train, iterations=2000, learning_rate=0.0001)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVACw_YJLvmx",
        "outputId": "662cdea0-9a30-43de-a937-250ef783bc1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamaño del vocabulario: 123381\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open(\"/content/drive/MyDrive/Datasets/AITextification/B_embeds_model.pkl\", \"wb\") as model_file:\n",
        "    pickle.dump(B_embeds_model, model_file)\n",
        "\n",
        "with open(\"/content/drive/MyDrive/Datasets/AITextification/B_word2Index.pkl\", \"wb\") as word2Index_file:\n",
        "    pickle.dump(B_word2Index, word2Index_file)\n",
        "\n",
        "pd.DataFrame(B_embeds).to_csv(\"/content/drive/MyDrive/Datasets/AITextification/B_embeds.csv\", index=False)"
      ],
      "metadata": {
        "id": "X-bJSXkjT3i9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [C]"
      ],
      "metadata": {
        "id": "1lTrOMFFLv-_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embeddings C: Dataset CD => Embeddings Gensim:\n",
        "C_gensim_df, C_gensim_model = gensim_embeddings(CD_train, dimensions=50, window=5, min_count=1, workers=4)"
      ],
      "metadata": {
        "id": "irty3ZjuLv_A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "C_gensim_model.save(\"/content/drive/MyDrive/Datasets/AITextification/C_gensim_model.bin\")\n",
        "C_gensim_df[\"embeddings\"].to_csv(\"/content/drive/MyDrive/Datasets/AITextification/C_gensim_embeddings.csv\", index=False)"
      ],
      "metadata": {
        "id": "_T9XR9srNi3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [D]"
      ],
      "metadata": {
        "id": "kiTq-mQ2LwOz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embeddings D: Dataset CD => Embeddings Propios:\n",
        "D_embeds, D_word2Index, D_embeds_model = own_embeddings(CD_train, iterations=2000, learning_rate=0.0001)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vjSS_6nYLwO0",
        "outputId": "e4e4bddc-1a04-42a4-f348-0096bcc6782c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamaño del vocabulario: 135214\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open(\"/content/drive/MyDrive/Datasets/AITextification/D_embeds_model.pkl\", \"wb\") as model_file:\n",
        "    pickle.dump(D_embeds_model, model_file)\n",
        "\n",
        "with open(\"/content/drive/MyDrive/Datasets/AITextification/D_word2Index.pkl\", \"wb\") as word2Index_file:\n",
        "    pickle.dump(D_word2Index, word2Index_file)\n",
        "\n",
        "pd.DataFrame(D_embeds).to_csv(\"/content/drive/MyDrive/Datasets/AITextification/D_embeds.csv\", index=False)"
      ],
      "metadata": {
        "id": "Pj48rBSmUY59"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [04] 🔳 Embeddings [B]"
      ],
      "metadata": {
        "id": "FVZ9g1y9ZD7X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset AB:\n",
        "TaskB_AB_train = pd.read_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskB-TrainAB.csv\")\n",
        "# AB_test = pd.read_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskB-DevAB.csv\")\n",
        "\n",
        "# Dataset CD:\n",
        "TaskB_CD_train = pd.read_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskB-TrainCD.csv\")\n",
        "# CD_test = pd.read_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskB-DevCD.csv\")"
      ],
      "metadata": {
        "id": "LRGjc24IZD7Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Embeddings de Datasets"
      ],
      "metadata": {
        "id": "m172zkpaZiZO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [A]"
      ],
      "metadata": {
        "id": "pgiFxTMSZiZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embeddings A: Dataset AB => Embeddings Gensim:\n",
        "TB_A_gensim_df, TB_A_gensim_model = gensim_embeddings(TaskB_AB_train, dimensions=50, window=5, min_count=1, workers=4)"
      ],
      "metadata": {
        "id": "B6Td9TvWZiZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TB_A_gensim_model.save(\"/content/drive/MyDrive/Datasets/AITextification/TaskB_A_gensim_model.bin\")\n",
        "TB_A_gensim_df[\"embeddings\"].to_csv(\"/content/drive/MyDrive/Datasets/AITextification/TaskB_A_gensim_embeddings.csv\", index=False)"
      ],
      "metadata": {
        "id": "xpL6RIFxZiZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [B]"
      ],
      "metadata": {
        "id": "ZmmeZgrNZiZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embeddings B: Dataset AB => Embeddings Propios:\n",
        "TB_B_embeds, TB_B_word2Index, TB_B_embeds_model = own_embeddings(TaskB_AB_train, iterations=2000, learning_rate=0.0001)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15cd6a40-3f91-42db-ff40-77da11443522",
        "id": "S1naB7QkZiZR"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamaño del vocabulario: 68731\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open(\"/content/drive/MyDrive/Datasets/AITextification/TaskB_B_embeds_model.pkl\", \"wb\") as model_file:\n",
        "    pickle.dump(TB_B_embeds_model, model_file)\n",
        "\n",
        "with open(\"/content/drive/MyDrive/Datasets/AITextification/TaskB_B_word2Index.pkl\", \"wb\") as word2Index_file:\n",
        "    pickle.dump(TB_B_word2Index, word2Index_file)\n",
        "\n",
        "pd.DataFrame(TB_B_embeds).to_csv(\"/content/drive/MyDrive/Datasets/AITextification/TB_B_embeds.csv\", index=False)"
      ],
      "metadata": {
        "id": "K860uD0wZiZR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [C]"
      ],
      "metadata": {
        "id": "qBnG0zhnZiZS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embeddings C: Dataset CD => Embeddings Gensim:\n",
        "TB_C_gensim_df, TB_C_gensim_model = gensim_embeddings(TaskB_CD_train, dimensions=50, window=5, min_count=1, workers=4)"
      ],
      "metadata": {
        "id": "-ysxVBEaZiZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TB_C_gensim_model.save(\"/content/drive/MyDrive/Datasets/AITextification/TB_C_gensim_model.bin\")\n",
        "TB_C_gensim_df[\"embeddings\"].to_csv(\"/content/drive/MyDrive/Datasets/AITextification/TB_C_gensim_embeddings.csv\", index=False)"
      ],
      "metadata": {
        "id": "uorOKaVCZiZS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### [D]"
      ],
      "metadata": {
        "id": "oBIPvgDAZiZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embeddings D: Dataset CD => Embeddings Propios:\n",
        "TB_D_embeds, TB_D_word2Index, TB_D_embeds_model = own_embeddings(TaskB_CD_train, iterations=2000, learning_rate=0.0001)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf6ef253-4528-47d4-8db6-f701f2994a54",
        "id": "TPtriMtJZiZT"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tamaño del vocabulario: 76948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open(\"/content/drive/MyDrive/Datasets/AITextification/TB_D_embeds_model.pkl\", \"wb\") as model_file:\n",
        "    pickle.dump(TB_D_embeds_model, model_file)\n",
        "\n",
        "with open(\"/content/drive/MyDrive/Datasets/AITextification/TB_D_word2Index.pkl\", \"wb\") as word2Index_file:\n",
        "    pickle.dump(TB_D_word2Index, word2Index_file)\n",
        "\n",
        "pd.DataFrame(TB_D_embeds).to_csv(\"/content/drive/MyDrive/Datasets/AITextification/TB_D_embeds.csv\", index=False)"
      ],
      "metadata": {
        "id": "xi-wHIlsZiZU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# [04] 👬 Autores\n",
        "## Realizado por:\n",
        "* León Rosas Manuel Alejandro.\n",
        "* Ramos Herrera Iván Alejandro."
      ],
      "metadata": {
        "id": "4DinJz64wzft"
      }
    }
  ]
}