Chapter 03.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#1 Text Classification with AI Platform"
      ],
      "metadata": {
        "id": "l08U4JSIUPd8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_uIYqsCVUHGy"
      },
      "outputs": [],
      "source": [
        "from google.cloud import aiplatform\n",
        "# Initialize the AI Platform client\n",
        "client = aiplatform.gapic.PipelineServiceClient(client_options={\"api_endpoint\": \"us-central1-aiplatform.googleapis.com\"})\n",
        "# Define the model deployment parameters\n",
        "model_deployment = {\n",
        "    \"display_name\": \"text_classification_model\",\n",
        "    \"model\": \"gs://your-bucket/model\",\n",
        "    \"endpoint\": \"projects/your-project/locations/us-central1/endpoints/your-endpoint\",\n",
        "}\n",
        "# Deploy the model\n",
        "response = client.deploy_model(model_deployment)\n",
        "print(\"Model deployed:\", response.result())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2 Sentiment Analysis with Natural Language API"
      ],
      "metadata": {
        "id": "B2MjOHckUhkM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.cloud import language_v1\n",
        "# Initialize the client\n",
        "client = language_v1.LanguageServiceClient()\n",
        "# Define the text to analyze\n",
        "text_content = \"I love using Google Cloud Platform for machine learning!\"\n",
        "# Create a document object\n",
        "document = language_v1.Document(content=text_content, type_=language_v1.Document.Type.PLAIN_TEXT)\n",
        "# Perform sentiment analysis\n",
        "response = client.analyze_sentiment(request={\"document\": document})\n",
        "# Print the sentiment score\n",
        "print(\"Sentiment score:\", response.document_sentiment.score)\n"
      ],
      "metadata": {
        "id": "UVpKLOllUtk1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3 Sentiment Analysis using BERT"
      ],
      "metadata": {
        "id": "RbrtZ_jBVRdG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ooD0wKz5Veet",
        "outputId": "5b69e9d0-cf3f-4bf6-bca3-6b7a4eaef7b0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "from transformers import Trainer, TrainingArguments\n",
        "# Load the tokenizer and model\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "# Tokenize the input text\n",
        "inputs = tokenizer(\"I love using transformer models!\", return_tensors=\"pt\")\n",
        "# Make predictions\n",
        "outputs = model(**inputs)\n",
        "logits = outputs.logits\n",
        "print(logits)"
      ],
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        "outputId": "87e743f9-b485-42a8-d48f-cb2867877859"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c46c4d6e3a284fc68108c3c1883d8b9d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-0.4711,  0.2273]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4 Text Classification using RoBERTa"
      ],
      "metadata": {
        "id": "c2WxYRxmV2bn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import RobertaTokenizer, RobertaForSequenceClassification\n",
        "# Load the tokenizer and model\n",
        "tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
        "model = RobertaForSequenceClassification.from_pretrained('roberta-base')\n",
        "# Tokenize the input text\n",
        "inputs = tokenizer(\"The movie was fantastic!\", return_tensors=\"pt\")\n",
        "# Make predictions\n",
        "outputs = model(**inputs)\n",
        "logits = outputs.logits\n",
        "print(logits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
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      },
      "execution_count": 4,
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        {
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        {
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              "model_id": "d962c736263f4feebaf60d2d91bf7b88"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "015b94903e6242ddbc934837cbddefec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-0.0632,  0.0735]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5 Text Generation using GPT"
      ],
      "metadata": {
        "id": "vKDh9GshWJvV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "# Load the tokenizer and model\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "# Encode the input prompt\n",
        "inputs = tokenizer(\"Once upon a time\", return_tensors=\"pt\")\n",
        "# Generate text\n",
        "outputs = model.generate(inputs.input_ids, max_length=50)\n",
        "generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "print(generated_text)"
      ],
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
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              "model_id": "b616feb4ca904762bc5bf9a86c294933"
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          "metadata": {}
        },
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          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ea7a2b9bc7a2469eb5d37b92a9d3c77d"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "f4c19d997ac94ae9aa75714757b9c5d2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8e9da37dc1840c4b64e9a1de0305ff7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63f5dfab87d649a6a7b186fb7d5ab1ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Once upon a time, the world was a place of great beauty and great danger. The world was a place of great danger, and the world was a place of great danger. The world was a place of great danger, and the world was a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#6 Question Answering using GPT-3"
      ],
      "metadata": {
        "id": "-xj9-441WbF4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0J44FEvGW0GD",
        "outputId": "8a3292c4-221e-4a7d-a9f7-3b7f9e16111c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m318.9/318.9 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: jiter, h11, httpcore, httpx, openai\n",
            "Successfully installed h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jiter-0.5.0 openai-1.50.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "from openai import OpenAI\n",
        "\n",
        "# Initialize the OpenAI client with your API key\n",
        "client = OpenAI(api_key='YOUR_API_KEY')\n",
        "\n",
        "# Define the prompt or message you want to send to the chat model\n",
        "messages = [\n",
        "    {\"role\": \"system\", \"content\": \"You are an assistant that helps answer user queries.\"},\n",
        "    {\"role\": \"user\", \"content\": \"What is the capital of India?\"}\n",
        "]\n",
        "\n",
        "# Generate the response using the 'chat.completions.create()' method\n",
        "completion = client.chat.completions.create(\n",
        "    model=\"gpt-3.5-turbo\",  # Use \"gpt-4\" if you want to use GPT-4 model\n",
        "    messages=messages\n",
        ")\n",
        "\n",
        "# Extract the generated message from the response\n",
        "generated_text = completion.choices[0].message.content\n",
        "\n",
        "# Print the response\n",
        "print(generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyFU-EKDWPi2",
        "outputId": "df7f6309-7af7-4e36-d014-d69e208545da"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The capital of India is New Delhi.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#7 Text Summarization using T5"
      ],
      "metadata": {
        "id": "YrpSi7vyaZy2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
        "# Load the tokenizer and model\n",
        "tokenizer = T5Tokenizer.from_pretrained('t5-small')\n",
        "model = T5ForConditionalGeneration.from_pretrained('t5-small')\n",
        "# Encode the input text\n",
        "inputs = tokenizer(\"summarize: The quick brown fox jumps over the lazy dog.\", return_tensors=\"pt\")\n",
        "# Generate summary\n",
        "outputs = model.generate(inputs.input_ids, max_length=50)\n",
        "summary = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "print(summary)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264,
          "referenced_widgets": [
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        },
        "id": "cK-g0-pFWwsS",
        "outputId": "c801e7ed-e858-4c65-f33d-e8e0b28bc497"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "015b56dfe3744c6c8494495ebc3ab0e8"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a00dc00c51b64cd5b4473486b41098af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/242M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the quick brown fox jumps over the lazy dog.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#8 Machine Translation using BART"
      ],
      "metadata": {
        "id": "wGc0sh3iaxrJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BartTokenizer, BartForConditionalGeneration\n",
        "# Load the tokenizer and model\n",
        "tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')\n",
        "model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')\n",
        "# Encode the input text\n",
        "inputs = tokenizer(\"translate English to French: The cat is on the mat.\", return_tensors=\"pt\")\n",
        "# Generate translation\n",
        "outputs = model.generate(inputs.input_ids, max_length=50)\n",
        "translation = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "print(translation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
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        "outputId": "f9c41fd9-e215-4e42-dfdd-36b977b34be1"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "fe9396a943984b339cbf947b83df6c0a"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7eb6d858193142f985b6545a689463dd"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.63k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c05590e884914e72bb180e946bbaad2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/1.02G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "291a5a24a30e4dfda4d0dc5d54ab855c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "translate English to French: The cat is on the mat.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#9 Masking Example"
      ],
      "metadata": {
        "id": "eRUvwK7qbCQ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertTokenizer, BertForMaskedLM\n",
        "\n",
        "# Load the tokenizer and model for masked language modeling\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "model = BertForMaskedLM.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Tokenize the input text with a [MASK] token\n",
        "inputs = tokenizer(\"The quick [MASK] fox jumps over the lazy dog.\", return_tensors=\"pt\")\n",
        "\n",
        "# Get model predictions\n",
        "with torch.no_grad():  # Disable gradient calculations for inference\n",
        "    outputs = model(**inputs)\n",
        "\n",
        "# Extract the logits for the masked token (position of the mask)\n",
        "mask_token_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]\n",
        "\n",
        "# Get the predicted token (most likely one)\n",
        "predicted_token_id = outputs.logits[0, mask_token_index].argmax(axis=-1)\n",
        "\n",
        "# Convert the token ID back to the corresponding word\n",
        "predicted_token = tokenizer.convert_ids_to_tokens(predicted_token_id)\n",
        "\n",
        "# Print the predicted word\n",
        "print(f\"Predicted token for the masked word: {predicted_token[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "al5n2EkgawAg",
        "outputId": "7c8326ef-5b07-4b48-9758-9cd7be13a8b2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted token for the masked word: thinking\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#10 Autoregressive Language Modeling using GPT"
      ],
      "metadata": {
        "id": "WG-mH2ysbxzY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "\n",
        "# Load the tokenizer and model\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "\n",
        "# Tokenize the input text\n",
        "inputs = tokenizer(\"The quick brown fox jumps\", return_tensors=\"pt\")\n",
        "\n",
        "# Get model predictions\n",
        "with torch.no_grad():  # Disable gradient calculation for inference\n",
        "    outputs = model(**inputs)\n",
        "\n",
        "# Get the predicted token ID for the last token in the input\n",
        "predicted_token_id = torch.argmax(outputs.logits[:, -1, :], dim=-1).item()\n",
        "\n",
        "# Convert the predicted token ID back to a word\n",
        "predicted_token = tokenizer.convert_ids_to_tokens(predicted_token_id)\n",
        "\n",
        "# Print the predicted word\n",
        "print(f\"Predicted token: {predicted_token}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uT428Q7LbMmx",
        "outputId": "6677ae62-5b38-416d-c96a-278004daf012"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted token: Ġup\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#11 model with 24 transformer layers split across four GPUs"
      ],
      "metadata": {
        "id": "G92rTnYKcOgp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "# Define a simple transformer model with 24 layers\n",
        "class SimpleTransformer(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SimpleTransformer, self).__init__()\n",
        "        self.layers = nn.ModuleList([nn.Transformer() for _ in range(24)])\n",
        "\n",
        "    def forward(self, x):\n",
        "        for layer in self.layers:\n",
        "            x = layer(x)\n",
        "        return x\n",
        "# Initialize the model and move to GPU\n",
        "model = SimpleTransformer()\n",
        "# Split the model across four GPUs\n",
        "device_ids = [0, 1, 2, 3]\n",
        "model = nn.DataParallel(model, device_ids=device_ids)\n"
      ],
      "metadata": {
        "id": "08H3hMIIb7jJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#12 BERT model trained on four GPUs"
      ],
      "metadata": {
        "id": "fib8Aavocikq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertForSequenceClassification, AdamW\n",
        "# Initialize the model and optimizer\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "# Move model to GPU and use DataParallel\n",
        "model = nn.DataParallel(model)\n",
        "model = model.to('cuda')\n",
        "# Define data loader\n",
        "train_loader = ...\n",
        "# Training loop\n",
        "for data in train_loader:\n",
        "    inputs, labels = data\n",
        "    inputs = inputs.to('cuda')\n",
        "    labels = labels.to('cuda')\n",
        "    optimizer.zero_grad()\n",
        "    outputs = model(inputs)\n",
        "    loss = outputs.loss\n",
        "    loss.backward()\n",
        "    optimizer.step()\n"
      ],
      "metadata": {
        "id": "EadT9wsgcnUZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#13 mixed-precision training with PyTorch's automatic mixed precision (AMP)"
      ],
      "metadata": {
        "id": "X68D2fD_ctRp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertForSequenceClassification, AdamW\n",
        "from torch.cuda.amp import GradScaler, autocast\n",
        "# Initialize the model and optimizer\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "model = model.to('cuda')\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
        "scaler = GradScaler()\n",
        "# Training loop\n",
        "for data in train_loader:\n",
        "    inputs, labels = data\n",
        "    inputs = inputs.to('cuda')\n",
        "    labels = labels.to('cuda')\n",
        "    optimizer.zero_grad()\n",
        "    with autocast():\n",
        "        outputs = model(inputs)\n",
        "        loss = outputs.loss\n",
        "    scaler.scale(loss).backward()\n",
        "    scaler.step(optimizer)\n",
        "    scaler.update()\n"
      ],
      "metadata": {
        "id": "AVDTOJc2cyWs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#14 Applying pruning to a BERT model"
      ],
      "metadata": {
        "id": "3CCF9TfKc2_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertForSequenceClassification\n",
        "# Load a pre-trained BERT model\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "# Apply pruning (example: pruning 20% of the neurons in each layer)\n",
        "prune_ratio = 0.2\n",
        "for name, module in model.named_modules():\n",
        "    if isinstance(module, torch.nn.Linear):\n",
        "        torch.nn.utils.prune.l1_unstructured(module, name='weight', amount=prune_ratio)\n",
        "# Check the sparsity of the model\n",
        "for name, module in model.named_modules():\n",
        "    if isinstance(module, torch.nn.Linear):\n",
        "        print(f\"Sparsity in {name}: {100. * float(torch.sum(module.weight == 0)) / module.weight.nelement()}%\")\n"
      ],
      "metadata": {
        "id": "PRD_sUfkc7QB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#15 Using use BFLOAT16 for training a neural network with TensorFlow."
      ],
      "metadata": {
        "id": "Rk76eC1pc-6o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "# Check if BFLOAT16 is available on the current hardware\n",
        "print(tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16'))\n",
        "# Set the policy to mixed_bfloat16\n",
        "policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')\n",
        "tf.keras.mixed_precision.experimental.set_policy(policy)\n",
        "# Define a simple model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(512, activation='relu', input_shape=(784,)),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])\n",
        "# Compile the model with BFLOAT16 policy\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "# Create a dataset\n",
        "(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0\n",
        "# Train the model\n",
        "model.fit(train_images, train_labels, epochs=5, validation_data=(test_images, test_labels))\n"
      ],
      "metadata": {
        "id": "LZ0JVZbldGs2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#16 Using multiple GPUs with PyTorch's DataParallel."
      ],
      "metadata": {
        "id": "z8VhJRE-dKMw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BertForSequenceClassification\n",
        "# Load the model\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "# Move the model to GPU and use DataParallel\n",
        "model = model.to('cuda')\n",
        "model = torch.nn.DataParallel(model)\n",
        "# Check the number of GPUs\n",
        "print(f\"Using {torch.cuda.device_count()} GPUs\")\n"
      ],
      "metadata": {
        "id": "FuG5gHW0dO71"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#17 Using TPUs with the transformers library and torch_xla"
      ],
      "metadata": {
        "id": "MQwnXtawdRua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch_xla\n",
        "import torch_xla.core.xla_model as xm\n",
        "from transformers import BertForSequenceClassification\n",
        "# Load the model\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "# Move the model to TPU\n",
        "device = xm.xla_device()\n",
        "model.to(device)\n",
        "# Training loop (example for a single batch)\n",
        "for data in train_loader:\n",
        "    inputs, labels = data\n",
        "    inputs = inputs.to(device)\n",
        "    labels = labels.to(device)\n",
        "    optimizer.zero_grad()\n",
        "    outputs = model(inputs)\n",
        "    loss = outputs.loss\n",
        "    loss.backward()\n",
        "    xm.optimizer_step(optimizer)\n"
      ],
      "metadata": {
        "id": "hWMHROgFdU_m"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}