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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lI1wKS0YaVzb",
        "outputId": "0ae6a30e-498a-46be-d645-561f3014358b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.33.1-py3-none-any.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m40.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.17.1 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.1\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers torch"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tokenizers"
      ],
      "metadata": {
        "id": "t9P8CdUndhE6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"sshleifer/distilbart-cnn-12-6\")"
      ],
      "metadata": {
        "id": "yjqrnFiOja6o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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          ]
        },
        "outputId": "c9039ca7-c9cf-48bf-8915-f374274299a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_inputs =     [\n",
        "        \"I love deep learning!\",\n",
        "        \"I hate this so much!\",\n",
        "    ]\n",
        "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "print(inputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lfkOaXWAdlTY",
        "outputId": "c1763004-efe5-4c36-be62-747ec204bd34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[   0,  100,  657, 1844, 2239,  328,    2,    1],\n",
            "        [   0,  100, 4157,   42,   98,  203,  328,    2]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 0],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Tokenizer output for \"I love deep learning!\"')\n",
        "print(f\"Input ids: {inputs['input_ids'][0]}\")\n",
        "print(f\"Attention Mask: {inputs['attention_mask'][0]}\")\n",
        "print(\"-\"*100)\n",
        "print('Tokenizer output for \"I hate this so much!\"')\n",
        "print(f\"Input ids: {inputs['input_ids'][1]}\")\n",
        "print(f\"Attention Mask: {inputs['attention_mask'][1]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SL_kmVosoZY",
        "outputId": "407372f5-a523-46db-ad73-f5f6c32c4bd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenizer output for \"I love deep learning!\"\n",
            "Input ids: tensor([   0,  100,  657, 1844, 2239,  328,    2,    1])\n",
            "Attention Mask: tensor([1, 1, 1, 1, 1, 1, 1, 0])\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Tokenizer output for \"I hate this so much!\"\n",
            "Input ids: tensor([   0,  100, 4157,   42,   98,  203,  328,    2])\n",
            "Attention Mask: tensor([1, 1, 1, 1, 1, 1, 1, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer('I love deep learning!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXnEEpPpsKi4",
        "outputId": "aaef3225-439c-42c2-d903-c5347813c465"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 100, 657, 1844, 2239, 328, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tokenizers Under The Hood"
      ],
      "metadata": {
        "id": "uJafxH3uthbR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.tokenize('I love deep learning!')\n",
        "tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDlurAjTtjQt",
        "outputId": "b28a17ca-294e-45ba-e3df-3567d143a8db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['I', 'Ġlove', 'Ġdeep', 'Ġlearning', '!']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "token_ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "token_ids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JZx7Io2tr1C",
        "outputId": "477887b8-47da-4c93-f592-0b39ba947f79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[100, 657, 1844, 2239, 328]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decoded_tokens = tokenizer.decode(token_ids)\n",
        "decoded_tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "2Yo4A1oDuDoB",
        "outputId": "8fbf4a9b-906f-40ed-980f-b1291d335251"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'I love deep learning!'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_prepped_ids = tokenizer.prepare_for_model(token_ids)\n",
        "model_prepped_ids"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90QbtQ6Jt_Jv",
        "outputId": "29169679-0cdb-405f-bac8-b39fc774d271"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BartTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [0, 100, 657, 1844, 2239, 328, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Models"
      ],
      "metadata": {
        "id": "DwfrlC1kd65c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pipeline"
      ],
      "metadata": {
        "id": "omt2oyLlqFKt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\")\n",
        "classifier(\n",
        "    [\n",
        "        \"I love deep learning!\",\n",
        "        \"I hate this so much!\",\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
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            "07eaad9e037441ac84a2aeeef0aca306"
          ]
        },
        "id": "fjsew7FadXFv",
        "outputId": "df16f47d-32b6-4b54-c51e-8111cb715f68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c99b85436f047928f0dccd0c114b9b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "634547502fb54a3d932c6110ef7de815"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30c755b407d64b1bad5243fd7ab58242"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d8a1571df52448b978cad5fcb237cc3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9998645782470703},\n",
              " {'label': 'NEGATIVE', 'score': 0.9994558691978455}]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text_generator = pipeline(\"text-generation\")\n",
        "\n",
        "text_generator([\n",
        "    'I went to the store to buy',\n",
        "    'When two objects in space get close to each other'\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348,
          "referenced_widgets": [
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        },
        "id": "xhjxvOQaqI7k",
        "outputId": "26255b4e-5c75-413c-a295-008af87ede4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to gpt2 and revision 6c0e608 (https://huggingface.co/gpt2).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abad7ba1372e4c7dbd0ef63a7f0d8f84"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "576eec3455cd4bbc992f38da6462fcd1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4fbe51164b84f1b90fd4d1f93e4ab21"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9cd1f7bc05a541aaa18bfa5fc3f2f9d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee3298f6051f474e820d8b3d8632e3af"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/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": "f498b01655784d078aadf50b35060670"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'generated_text': 'I went to the store to buy the car and when I opened the door I was met with an angry woman from the front of the shop with a bottle opener.\\n\\n\"Oh my god!\" I screamed. \"What\\'s happening to you?\"'}],\n",
              " [{'generated_text': 'When two objects in space get close to each other the physics kicks on. The other object jumps. After this happens the physics stops causing any collision, and when the collision happens the physics will continue going and we will see the two objects as they collide'}]]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summarizer = pipeline(\"summarization\")\n",
        "\n",
        "summarizer([\n",
        "    \"\"\"A Fibonacci heap is a collection of trees satisfying the min-heap property. It allows faster amortized time for many operations than binary or binomial heaps.\n",
        "    Trees in a Fibonacci heap can have any shape, which facilitates efficient operations. Lazy strategies are employed: node removals and consolidations are delayed until\n",
        "    absolutely necessary (like during an extract-min operation). The main advantage lies in decreasing a key and merging two heaps, which are constant and amortized\n",
        "    constant time, respectively. Nodes have a \"mark\" indicating if they've lost a child since the last time they were made a child of another node, assisting in\n",
        "    restructuring during operations.\"\"\"\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdvAE7atqdIx",
        "outputId": "83c0b456-0dff-4f33-bf42-a2020c84d66c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': ' A Fibonacci heap is a collection of trees satisfying the min-heap property . It allows faster amortized time for many operations than binary or binomial heaps . Nodes have a \"mark\" indicating if they\\'ve lost a child since the last time they were made a child of another node .'}]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accessing Pretrained models"
      ],
      "metadata": {
        "id": "1jkCLo6bwoeo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load model directly\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")\n",
        "model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")"
      ],
      "metadata": {
        "id": "Trri6_fMzE1t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer('I love deep learning', return_tensors='pt')\n",
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MM9GZ1jjzUoi",
        "outputId": "3776ceef-04c3-4d15-a362-f3b48833d682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[ 101, 1045, 2293, 2784, 4083,  102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model(**inputs)\n",
        "outputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Q0-0DqZzqpq",
        "outputId": "859d9c0e-71c3-4c70-8d80-d815b2b56808"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SequenceClassifierOutput(loss=None, logits=tensor([[-4.1975,  4.4937]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Embeddings"
      ],
      "metadata": {
        "id": "11SrR5Ayv570"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel\n",
        "\n",
        "model = AutoModel.from_pretrained(\"distilbert-base-uncased-finetuned-sst-2-english\")"
      ],
      "metadata": {
        "id": "JKeZ3SRBTiVo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer('I love deep learning!', padding=True, truncation=True, return_tensors='pt')\n",
        "\n",
        "outputs = model(**inputs)\n",
        "\n",
        "print(outputs.last_hidden_state.shape) # the token embeddings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QR74Oi83d8Rz",
        "outputId": "721f1a49-af8e-47c5-a918-85271fb0bd93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 7, 768])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to get the full context vector for the sequence\n",
        "context_vectors = outputs.last_hidden_state.mean(dim=1)\n",
        "context_vectors.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zc3QOBSpeTki",
        "outputId": "e41ca93c-9daf-4a0c-e24c-c42a514ab5b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 768])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Accessing Model Config & Creating Custom Models"
      ],
      "metadata": {
        "id": "QeICQFXEieNU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2Config, GPT2Model\n",
        "\n",
        "# Building the config\n",
        "config = GPT2Config()"
      ],
      "metadata": {
        "id": "oMaQWlmRg3Ps"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jy42HChXihxd",
        "outputId": "08691d35-dc04-41a8-c9ca-a50bfb020a8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPT2Config {\n",
            "  \"activation_function\": \"gelu_new\",\n",
            "  \"attn_pdrop\": 0.1,\n",
            "  \"bos_token_id\": 50256,\n",
            "  \"embd_pdrop\": 0.1,\n",
            "  \"eos_token_id\": 50256,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"layer_norm_epsilon\": 1e-05,\n",
            "  \"model_type\": \"gpt2\",\n",
            "  \"n_embd\": 768,\n",
            "  \"n_head\": 12,\n",
            "  \"n_inner\": null,\n",
            "  \"n_layer\": 12,\n",
            "  \"n_positions\": 1024,\n",
            "  \"reorder_and_upcast_attn\": false,\n",
            "  \"resid_pdrop\": 0.1,\n",
            "  \"scale_attn_by_inverse_layer_idx\": false,\n",
            "  \"scale_attn_weights\": true,\n",
            "  \"summary_activation\": null,\n",
            "  \"summary_first_dropout\": 0.1,\n",
            "  \"summary_proj_to_labels\": true,\n",
            "  \"summary_type\": \"cls_index\",\n",
            "  \"summary_use_proj\": true,\n",
            "  \"transformers_version\": \"4.33.1\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 50257\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building the model from the config\n",
        "gpt_model = GPT2Model(config)"
      ],
      "metadata": {
        "id": "GVaNKIkMwQRA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving Models"
      ],
      "metadata": {
        "id": "5a14d094z22r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gpt_model.save_pretrained('gpt2_model')"
      ],
      "metadata": {
        "id": "DGOKa751tPNw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dAkJbJ3mUakm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}