11.NLP.ipynb

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        "***"
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      "cell_type": "markdown",
      "source": [
        "1.Basic"
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      "source": [
        "# Import necessary libraries\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize, sent_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
        "from nltk import pos_tag, ne_chunk\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import re\n",
        "\n",
        "# Ensure necessary resources are downloaded\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('maxent_ne_chunker')\n",
        "nltk.download('words')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "# Sample text\n",
        "text = \"Hello! My name is John. I'm learning NLP. NLP is an exciting field of AI.\"\n",
        "\n",
        "# Tokenization\n",
        "def tokenization_example(text):\n",
        "    print(\"\\nTokenization:\")\n",
        "    words = word_tokenize(text)\n",
        "    sentences = sent_tokenize(text)\n",
        "    print(\"Words:\", words)\n",
        "    print(\"Sentences:\", sentences)\n",
        "\n",
        "# Text Preprocessing: Lowercasing and Removing Punctuation\n",
        "def preprocessing_example(text):\n",
        "    print(\"\\nText Preprocessing:\")\n",
        "    text = text.lower()\n",
        "    text = re.sub(r'[^\\w\\s]', '', text)\n",
        "    print(\"Processed Text:\", text)\n",
        "    return text\n",
        "\n",
        "# Stop Words Removal\n",
        "def stopwords_removal_example(text):\n",
        "    print(\"\\nStop Words Removal:\")\n",
        "    words = word_tokenize(text)\n",
        "    filtered_words = [word for word in words if word not in stopwords.words('english')]\n",
        "    print(\"Filtered Words:\", filtered_words)\n",
        "    return filtered_words\n",
        "\n",
        "# POS Tagging\n",
        "def pos_tagging_example(words):\n",
        "    print(\"\\nPOS Tagging:\")\n",
        "    pos_tags = pos_tag(words)\n",
        "    print(\"POS Tags:\", pos_tags)\n",
        "\n",
        "# Named Entity Recognition (NER)\n",
        "def ner_example(text):\n",
        "    print(\"\\nNamed Entity Recognition:\")\n",
        "    words = word_tokenize(text)\n",
        "    pos_tags = pos_tag(words)\n",
        "    named_entities = ne_chunk(pos_tags)\n",
        "    print(named_entities)\n",
        "\n",
        "# Stemming and Lemmatization\n",
        "def stemming_and_lemmatization_example(words):\n",
        "    print(\"\\nStemming and Lemmatization:\")\n",
        "    porter_stemmer = PorterStemmer()\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    for word in words:\n",
        "        print(f\"{word} -> Stem: {porter_stemmer.stem(word)}, Lemma: {lemmatizer.lemmatize(word, pos='v')}\")\n",
        "\n",
        "# Text Encoding\n",
        "def text_encoding_example(documents):\n",
        "    print(\"\\nText Encoding:\")\n",
        "    # Bag of Words\n",
        "    vectorizer = CountVectorizer()\n",
        "    bow = vectorizer.fit_transform(documents)\n",
        "    print(\"Bag of Words:\\n\", bow.toarray())\n",
        "    print(\"Feature Names:\", vectorizer.get_feature_names_out())\n",
        "\n",
        "    # TF-IDF\n",
        "    tfidf_vectorizer = TfidfVectorizer()\n",
        "    tfidf = tfidf_vectorizer.fit_transform(documents)\n",
        "    print(\"\\nTF-IDF:\\n\", tfidf.toarray())\n",
        "    print(\"Feature Names:\", tfidf_vectorizer.get_feature_names_out())\n",
        "\n",
        "# Cosine Similarity\n",
        "def cosine_similarity_example(documents):\n",
        "    print(\"\\nCosine Similarity:\")\n",
        "    tfidf_vectorizer = TfidfVectorizer()\n",
        "    tfidf = tfidf_vectorizer.fit_transform(documents)\n",
        "    similarity_matrix = cosine_similarity(tfidf)\n",
        "    print(\"Cosine Similarity Matrix:\\n\", similarity_matrix)\n",
        "\n",
        "# Sample documents for encoding and cosine similarity\n",
        "documents = [\n",
        "    \"I love programming in Python. Python is great for NLP.\",\n",
        "    \"NLP is an exciting field. It involves processing text and speech.\",\n",
        "    \"Text preprocessing is an important step in NLP.\"\n",
        "]\n",
        "\n",
        "# Calling the functions\n",
        "tokenization_example(text)\n",
        "processed_text = preprocessing_example(text)\n",
        "filtered_words = stopwords_removal_example(processed_text)\n",
        "pos_tagging_example(filtered_words)\n",
        "ner_example(text)\n",
        "stemming_and_lemmatization_example(filtered_words)\n",
        "text_encoding_example(documents)\n",
        "cosine_similarity_example(documents)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLMkQKOcynZM",
        "outputId": "03ba4587-b302-478e-82bd-4b57c3fceb0c"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading package maxent_ne_chunker to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping chunkers/maxent_ne_chunker.zip.\n",
            "[nltk_data] Downloading package words to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/words.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Tokenization:\n",
            "Words: ['Hello', '!', 'My', 'name', 'is', 'John', '.', 'I', \"'m\", 'learning', 'NLP', '.', 'NLP', 'is', 'an', 'exciting', 'field', 'of', 'AI', '.']\n",
            "Sentences: ['Hello!', 'My name is John.', \"I'm learning NLP.\", 'NLP is an exciting field of AI.']\n",
            "\n",
            "Text Preprocessing:\n",
            "Processed Text: hello my name is john im learning nlp nlp is an exciting field of ai\n",
            "\n",
            "Stop Words Removal:\n",
            "Filtered Words: ['hello', 'name', 'john', 'im', 'learning', 'nlp', 'nlp', 'exciting', 'field', 'ai']\n",
            "\n",
            "POS Tagging:\n",
            "POS Tags: [('hello', 'NN'), ('name', 'NN'), ('john', 'NN'), ('im', 'NN'), ('learning', 'VBG'), ('nlp', 'JJ'), ('nlp', 'JJ'), ('exciting', 'VBG'), ('field', 'NN'), ('ai', 'NN')]\n",
            "\n",
            "Named Entity Recognition:\n",
            "(S\n",
            "  (GPE Hello/NN)\n",
            "  !/.\n",
            "  My/PRP$\n",
            "  name/NN\n",
            "  is/VBZ\n",
            "  (PERSON John/NNP)\n",
            "  ./.\n",
            "  I/PRP\n",
            "  'm/VBP\n",
            "  learning/VBG\n",
            "  (ORGANIZATION NLP/NNP)\n",
            "  ./.\n",
            "  (ORGANIZATION NLP/NNP)\n",
            "  is/VBZ\n",
            "  an/DT\n",
            "  exciting/JJ\n",
            "  field/NN\n",
            "  of/IN\n",
            "  (ORGANIZATION AI/NNP)\n",
            "  ./.)\n",
            "\n",
            "Stemming and Lemmatization:\n",
            "hello -> Stem: hello, Lemma: hello\n",
            "name -> Stem: name, Lemma: name\n",
            "john -> Stem: john, Lemma: john\n",
            "im -> Stem: im, Lemma: im\n",
            "learning -> Stem: learn, Lemma: learn\n",
            "nlp -> Stem: nlp, Lemma: nlp\n",
            "nlp -> Stem: nlp, Lemma: nlp\n",
            "exciting -> Stem: excit, Lemma: excite\n",
            "field -> Stem: field, Lemma: field\n",
            "ai -> Stem: ai, Lemma: ai\n",
            "\n",
            "Text Encoding:\n",
            "Bag of Words:\n",
            " [[0 0 0 0 1 1 0 1 0 1 0 1 1 0 0 1 2 0 0 0]\n",
            " [1 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 1]\n",
            " [1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 1 1]]\n",
            "Feature Names: ['an' 'and' 'exciting' 'field' 'for' 'great' 'important' 'in' 'involves'\n",
            " 'is' 'it' 'love' 'nlp' 'preprocessing' 'processing' 'programming'\n",
            " 'python' 'speech' 'step' 'text']\n",
            "\n",
            "TF-IDF:\n",
            " [[0.         0.         0.         0.         0.32833589 0.32833589\n",
            "  0.         0.24970791 0.         0.19392047 0.         0.32833589\n",
            "  0.19392047 0.         0.         0.32833589 0.65667178 0.\n",
            "  0.         0.        ]\n",
            " [0.25558364 0.33606177 0.33606177 0.33606177 0.         0.\n",
            "  0.         0.         0.33606177 0.1984835  0.33606177 0.\n",
            "  0.1984835  0.         0.33606177 0.         0.         0.33606177\n",
            "  0.         0.25558364]\n",
            " [0.32628714 0.         0.         0.         0.         0.\n",
            "  0.42902838 0.32628714 0.         0.25339107 0.         0.\n",
            "  0.25339107 0.42902838 0.         0.         0.         0.\n",
            "  0.42902838 0.32628714]]\n",
            "Feature Names: ['an' 'and' 'exciting' 'field' 'for' 'great' 'important' 'in' 'involves'\n",
            " 'is' 'it' 'love' 'nlp' 'preprocessing' 'processing' 'programming'\n",
            " 'python' 'speech' 'step' 'text']\n",
            "\n",
            "Cosine Similarity:\n",
            "Cosine Similarity Matrix:\n",
            " [[1.         0.07698002 0.17975191]\n",
            " [0.07698002 1.         0.2673752 ]\n",
            " [0.17975191 0.2673752  1.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.Intermediate"
      ],
      "metadata": {
        "id": "l5GHSOpMyruc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import nltk\n",
        "import spacy\n",
        "from gensim.models import Word2Vec, KeyedVectors\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, GRU\n",
        "from transformers import BertTokenizer, TFBertModel\n",
        "import numpy as np\n",
        "import re\n",
        "\n",
        "# Ensure necessary resources are downloaded\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# Load spaCy model\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "# Sample text\n",
        "text = \"Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that focuses on the interaction between computers and humans through natural language.\"\n",
        "\n",
        "# Advanced Embeddings\n",
        "def word2vec_example(sentences):\n",
        "    print(\"\\nWord2Vec Embeddings:\")\n",
        "    model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)\n",
        "    word_vectors = model.wv\n",
        "    print(\"Word2Vec vector for 'language':\", word_vectors['language'])\n",
        "\n",
        "# Prepare sample sentences for Word2Vec\n",
        "sentences = [\n",
        "    [\"natural\", \"language\", \"processing\", \"nlp\", \"field\", \"artificial\", \"intelligence\"],\n",
        "    [\"focuses\", \"interaction\", \"computers\", \"humans\", \"natural\", \"language\"]\n",
        "]\n",
        "\n",
        "# Load pre-trained word vectors\n",
        "def load_pretrained_word_vectors():\n",
        "    print(\"\\nLoading Pre-trained Word Vectors:\")\n",
        "    word_vectors = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
        "    print(\"Pre-trained vector for 'language':\", word_vectors['language'])\n",
        "\n",
        "# RNNs, LSTMs, and GRUs\n",
        "def rnn_lstm_gru_example():\n",
        "    print(\"\\nRNN, LSTM, and GRU Example:\")\n",
        "\n",
        "    # Sample data\n",
        "    sample_text = \"NLP is fun.\"\n",
        "    sample_text = re.sub(r'[^\\w\\s]', '', sample_text).lower().split()\n",
        "    word_index = {word: i for i, word in enumerate(set(sample_text))}\n",
        "    sequences = np.array([[word_index[word] for word in sample_text]])\n",
        "\n",
        "    # Model parameters\n",
        "    vocab_size = len(word_index)\n",
        "    embedding_dim = 8\n",
        "    input_length = len(sample_text)\n",
        "\n",
        "    # RNN Model\n",
        "    rnn_model = Sequential([\n",
        "        Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=input_length),\n",
        "        tf.keras.layers.SimpleRNN(10),\n",
        "        Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "    rnn_model.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "    print(\"RNN Model Summary:\")\n",
        "    rnn_model.summary()\n",
        "\n",
        "    # LSTM Model\n",
        "    lstm_model = Sequential([\n",
        "        Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=input_length),\n",
        "        LSTM(10),\n",
        "        Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "    lstm_model.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "    print(\"\\nLSTM Model Summary:\")\n",
        "    lstm_model.summary()\n",
        "\n",
        "    # GRU Model\n",
        "    gru_model = Sequential([\n",
        "        Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=input_length),\n",
        "        GRU(10),\n",
        "        Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "    gru_model.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "    print(\"\\nGRU Model Summary:\")\n",
        "    gru_model.summary()\n",
        "\n",
        "# Attention Mechanism\n",
        "def attention_mechanism_example():\n",
        "    print(\"\\nAttention Mechanism Example:\")\n",
        "\n",
        "    # Sample input\n",
        "    input_length = 5\n",
        "    input_dim = 10\n",
        "    timesteps = input_length\n",
        "    input_data = tf.random.normal((1, timesteps, input_dim))\n",
        "\n",
        "    # Define attention layer\n",
        "    attention = tf.keras.layers.Attention()\n",
        "    query = tf.keras.layers.Dense(input_dim)(input_data)\n",
        "    value = tf.keras.layers.Dense(input_dim)(input_data)\n",
        "    context_vector, attention_weights = attention([query, value], return_attention_scores=True)\n",
        "\n",
        "    print(\"Context Vector Shape:\", context_vector.shape)\n",
        "    print(\"Attention Weights Shape:\", attention_weights.shape)\n",
        "\n",
        "# Transformers and BERT\n",
        "def transformers_bert_example():\n",
        "    print(\"\\nTransformers and BERT Example:\")\n",
        "\n",
        "    # Load BERT model and tokenizer\n",
        "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "    model = TFBertModel.from_pretrained('bert-base-uncased')\n",
        "\n",
        "    # Sample text\n",
        "    sample_text = \"NLP is a fascinating field.\"\n",
        "\n",
        "    # Tokenize input\n",
        "    inputs = tokenizer(sample_text, return_tensors='tf')\n",
        "    outputs = model(inputs)\n",
        "\n",
        "    print(\"BERT Model Outputs Shape:\", outputs.last_hidden_state.shape)\n",
        "\n",
        "# Calling the functions\n",
        "word2vec_example(sentences)\n",
        "# Uncomment the following line after downloading the 'GoogleNews-vectors-negative300.bin' model\n",
        "# load_pretrained_word_vectors()\n",
        "rnn_lstm_gru_example()\n",
        "attention_mechanism_example()\n",
        "transformers_bert_example()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "outputId": "8a370d7d-345c-4f11-de68-17b7bbf20456"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Word2Vec Embeddings:\n",
            "Word2Vec vector for 'language': [-5.3622725e-04  2.3643136e-04  5.1033497e-03  9.0092728e-03\n",
            " -9.3029495e-03 -7.1168090e-03  6.4588725e-03  8.9729885e-03\n",
            " -5.0154282e-03 -3.7633716e-03  7.3805046e-03 -1.5334714e-03\n",
            " -4.5366134e-03  6.5540518e-03 -4.8601604e-03 -1.8160177e-03\n",
            "  2.8765798e-03  9.9187379e-04 -8.2852151e-03 -9.4488179e-03\n",
            "  7.3117660e-03  5.0702621e-03  6.7576934e-03  7.6286553e-04\n",
            "  6.3508903e-03 -3.4053659e-03 -9.4640139e-04  5.7685734e-03\n",
            " -7.5216377e-03 -3.9361035e-03 -7.5115822e-03 -9.3004224e-04\n",
            "  9.5381187e-03 -7.3191668e-03 -2.3337686e-03 -1.9377411e-03\n",
            "  8.0774371e-03 -5.9308959e-03  4.5162440e-05 -4.7537340e-03\n",
            " -9.6035507e-03  5.0072931e-03 -8.7595852e-03 -4.3918253e-03\n",
            " -3.5099984e-05 -2.9618145e-04 -7.6612402e-03  9.6147433e-03\n",
            "  4.9820580e-03  9.2331432e-03 -8.1579173e-03  4.4957981e-03\n",
            " -4.1370760e-03  8.2453608e-04  8.4986202e-03 -4.4621765e-03\n",
            "  4.5175003e-03 -6.7869602e-03 -3.5484887e-03  9.3985079e-03\n",
            " -1.5776526e-03  3.2137157e-04 -4.1406299e-03 -7.6826881e-03\n",
            " -1.5080082e-03  2.4697948e-03 -8.8802696e-04  5.5336617e-03\n",
            " -2.7429771e-03  2.2600652e-03  5.4557943e-03  8.3459532e-03\n",
            " -1.4537406e-03 -9.2081428e-03  4.3705525e-03  5.7178497e-04\n",
            "  7.4419081e-03 -8.1328274e-04 -2.6384138e-03 -8.7530091e-03\n",
            " -8.5655687e-04  2.8265631e-03  5.4014288e-03  7.0526563e-03\n",
            " -5.7031214e-03  1.8588197e-03  6.0888636e-03 -4.7980510e-03\n",
            " -3.1072604e-03  6.7976294e-03  1.6314756e-03  1.8991709e-04\n",
            "  3.4736372e-03  2.1777749e-04  9.6188262e-03  5.0606038e-03\n",
            " -8.9173904e-03 -7.0415605e-03  9.0145587e-04  6.3925339e-03]\n",
            "\n",
            "RNN, LSTM, and GRU Example:\n",
            "RNN Model Summary:\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 3, 8)              24        \n",
            "                                                                 \n",
            " simple_rnn (SimpleRNN)      (None, 10)                190       \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 225 (900.00 Byte)\n",
            "Trainable params: 225 (900.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "\n",
            "LSTM Model Summary:\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_1 (Embedding)     (None, 3, 8)              24        \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 10)                760       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 795 (3.11 KB)\n",
            "Trainable params: 795 (3.11 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "\n",
            "GRU Model Summary:\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding_2 (Embedding)     (None, 3, 8)              24        \n",
            "                                                                 \n",
            " gru (GRU)                   (None, 10)                600       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 635 (2.48 KB)\n",
            "Trainable params: 635 (2.48 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "\n",
            "Attention Mechanism Example:\n",
            "Context Vector Shape: (1, 5, 10)\n",
            "Attention Weights Shape: (1, 5, 5)\n",
            "\n",
            "Transformers and BERT Example:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias']\n",
            "- This IS expected if you are initializing TFBertModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the weights of TFBertModel were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BERT Model Outputs Shape: (1, 9, 768)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3.Advance"
      ],
      "metadata": {
        "id": "LaXzCuDMzo5F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import nltk\n",
        "import spacy\n",
        "from gensim.models import Word2Vec\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, GRU, Attention\n",
        "from transformers import BertTokenizer, TFBertModel\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.decomposition import LatentDirichletAllocation\n",
        "import numpy as np\n",
        "import re\n",
        "from nltk.translate.bleu_score import sentence_bleu\n",
        "from transformers import T5Tokenizer, TFT5ForConditionalGeneration\n",
        "\n",
        "# Ensure necessary resources are downloaded\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# Load spaCy model\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "# Sample text\n",
        "text = \"Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that focuses on the interaction between computers and humans through natural language.\"\n",
        "\n",
        "# Sequence-to-Sequence Models (Encoder-Decoder)\n",
        "def seq2seq_example():\n",
        "    print(\"\\nSequence-to-Sequence Example:\")\n",
        "    # Example sentence pairs for translation\n",
        "    input_texts = [\"Hello\", \"How are you?\", \"Goodbye\"]\n",
        "    target_texts = [\"Bonjour\", \"Comment ça va?\", \"Au revoir\"]\n",
        "\n",
        "    # Tokenization\n",
        "    tokenizer = tf.keras.preprocessing.text.Tokenizer()\n",
        "    tokenizer.fit_on_texts(input_texts + target_texts)\n",
        "    input_sequences = tokenizer.texts_to_sequences(input_texts)\n",
        "    target_sequences = tokenizer.texts_to_sequences(target_texts)\n",
        "\n",
        "    # Padding\n",
        "    input_sequences = tf.keras.preprocessing.sequence.pad_sequences(input_sequences, padding='post')\n",
        "    target_sequences = tf.keras.preprocessing.sequence.pad_sequences(target_sequences, padding='post')\n",
        "\n",
        "    # Encoder-Decoder Model\n",
        "    vocab_size = len(tokenizer.word_index) + 1\n",
        "    embedding_dim = 8\n",
        "    input_length = input_sequences.shape[1]\n",
        "    target_length = target_sequences.shape[1]\n",
        "\n",
        "    encoder_inputs = tf.keras.layers.Input(shape=(input_length,))\n",
        "    encoder_embedding = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim)(encoder_inputs)\n",
        "    encoder_lstm = LSTM(50, return_state=True)\n",
        "    _, state_h, state_c = encoder_lstm(encoder_embedding)\n",
        "    encoder_states = [state_h, state_c]\n",
        "\n",
        "    decoder_inputs = tf.keras.layers.Input(shape=(target_length,))\n",
        "    decoder_embedding = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim)(decoder_inputs)\n",
        "    decoder_lstm = LSTM(50, return_sequences=True, return_state=True)\n",
        "    decoder_outputs, _, _ = decoder_lstm(decoder_embedding, initial_state=encoder_states)\n",
        "    decoder_dense = Dense(vocab_size, activation='softmax')\n",
        "    decoder_outputs = decoder_dense(decoder_outputs)\n",
        "\n",
        "    model = tf.keras.models.Model([encoder_inputs, decoder_inputs], decoder_outputs)\n",
        "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\n",
        "\n",
        "    print(model.summary())\n",
        "\n",
        "# Text Summarization using T5\n",
        "def text_summarization_example():\n",
        "    print(\"\\nText Summarization Example:\")\n",
        "    summarizer = TFT5ForConditionalGeneration.from_pretrained('t5-small')\n",
        "    tokenizer = T5Tokenizer.from_pretrained('t5-small')\n",
        "\n",
        "    text = \"Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to enable computers to understand, interpret, and generate human languages in a way that is valuable.\"\n",
        "    input_ids = tokenizer.encode(\"summarize: \" + text, return_tensors=\"tf\")\n",
        "    summary_ids = summarizer.generate(input_ids, max_length=50, num_beams=5, early_stopping=True)\n",
        "    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)\n",
        "\n",
        "    print(\"Summary:\", summary)\n",
        "\n",
        "# Machine Translation using MarianMT\n",
        "def machine_translation_example():\n",
        "    print(\"\\nMachine Translation Example:\")\n",
        "    from transformers import MarianMTModel, MarianTokenizer\n",
        "\n",
        "    model_name = 'Helsinki-NLP/opus-mt-en-fr'\n",
        "    tokenizer = MarianTokenizer.from_pretrained(model_name)\n",
        "    model = MarianMTModel.from_pretrained(model_name)\n",
        "\n",
        "    text = \"NLP is a fascinating field.\"\n",
        "    translated = model.generate(**tokenizer(text, return_tensors=\"pt\", padding=True))\n",
        "    translation = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]\n",
        "\n",
        "    print(\"Translation:\", translation[0])\n",
        "\n",
        "# Topic Modeling using LDA\n",
        "def topic_modeling_example():\n",
        "    print(\"\\nTopic Modeling Example:\")\n",
        "    documents = [\n",
        "        \"I love reading books about AI and NLP.\",\n",
        "        \"Natural language processing is an interesting field.\",\n",
        "        \"Deep learning is a key technology in AI.\",\n",
        "        \"I enjoy studying machine learning and artificial intelligence.\"\n",
        "    ]\n",
        "\n",
        "    vectorizer = TfidfVectorizer(stop_words='english')\n",
        "    X = vectorizer.fit_transform(documents)\n",
        "\n",
        "    lda = LatentDirichletAllocation(n_components=2, random_state=42)\n",
        "    lda.fit(X)\n",
        "\n",
        "    terms = vectorizer.get_feature_names_out()\n",
        "    for idx, topic in enumerate(lda.components_):\n",
        "        print(f\"Topic {idx + 1}:\")\n",
        "        print(\" \".join([terms[i] for i in topic.argsort()[:-6:-1]]))\n",
        "\n",
        "# Calling the functions\n",
        "seq2seq_example()\n",
        "text_summarization_example()\n",
        "machine_translation_example()\n",
        "topic_modeling_example()\n"
      ],
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        "outputId": "d9559785-2740-4651-d727-99d977bd31e3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Sequence-to-Sequence Example:\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 3)]                  0         []                            \n",
            "                                                                                                  \n",
            " input_2 (InputLayer)        [(None, 3)]                  0         []                            \n",
            "                                                                                                  \n",
            " embedding_3 (Embedding)     (None, 3, 8)                 96        ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " embedding_4 (Embedding)     (None, 3, 8)                 96        ['input_2[0][0]']             \n",
            "                                                                                                  \n",
            " lstm_1 (LSTM)               [(None, 50),                 11800     ['embedding_3[0][0]']         \n",
            "                              (None, 50),                                                         \n",
            "                              (None, 50)]                                                         \n",
            "                                                                                                  \n",
            " lstm_2 (LSTM)               [(None, 3, 50),              11800     ['embedding_4[0][0]',         \n",
            "                              (None, 50),                            'lstm_1[0][1]',              \n",
            "                              (None, 50)]                            'lstm_1[0][2]']              \n",
            "                                                                                                  \n",
            " dense_5 (Dense)             (None, 3, 12)                612       ['lstm_2[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 24404 (95.33 KB)\n",
            "Trainable params: 24404 (95.33 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n",
            "None\n",
            "\n",
            "Text Summarization Example:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
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              "model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All PyTorch model weights were used when initializing TFT5ForConditionalGeneration.\n",
            "\n",
            "All the weights of TFT5ForConditionalGeneration were initialized from the PyTorch model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
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            "Summary: natural language processing (NLP) focuses on the interaction between computers and humans through natural language. the ultimate objective of NLP is to enable computers to understand, interpret, and generate human languages.\n",
            "\n",
            "Machine Translation Example:\n"
          ]
        },
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
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        {
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          "text": [
            "Translation: Le NLP est un domaine fascinant.\n",
            "\n",
            "Topic Modeling Example:\n",
            "Topic 1:\n",
            "learning deep key technology machine\n",
            "Topic 2:\n",
            "reading nlp love books language\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "****"
      ],
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}