BioGPTDemo.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rajkumar-surana/BASH-and-Vim-Tutorial/blob/main/BioGPTDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sacremoses"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKHzPO2Z183g",
        "outputId": "d11719dc-6eff-4e97-c3fa-ad02bb77faa1"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m880.6/880.6 kB\u001b[0m \u001b[31m31.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from sacremoses) (2022.10.31)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from sacremoses) (1.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from sacremoses) (8.1.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from sacremoses) (1.2.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sacremoses) (4.65.0)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895259 sha256=ca5ed6bd0bbb559cf524fa1c40e20954195bd0a6c258800377742dcf32d595aa\n",
            "  Stored in directory: /root/.cache/pip/wheels/00/24/97/a2ea5324f36bc626e1ea0267f33db6aa80d157ee977e9e42fb\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses\n",
            "Successfully installed sacremoses-0.0.53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iLi9dPyC1U9s",
        "outputId": "ffcebf9b-ba87-4b97-d758-8e89e97ed95b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m31.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m28.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m97.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (2023.4.0)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, set_seed"
      ],
      "metadata": {
        "id": "smZeNFWR1kTE"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BioGptTokenizer, BioGptForCausalLM"
      ],
      "metadata": {
        "id": "Pw5K7da_1mkU"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = BioGptForCausalLM.from_pretrained(\"microsoft/biogpt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "outputId": "47b99903-aa56-457e-c663-d1dc2a324cec"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/595 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de4aecdcd15944349a67ac1e892b6f05"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.56G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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    {
      "cell_type": "code",
      "source": [
        "tokenizer = BioGptTokenizer.from_pretrained(\"microsoft/biogpt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "MLghAhMo1vYS",
        "outputId": "a5f1e091-cefe-457a-dc7e-b82c51698cbf"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/927k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ce4695a923c43d08f0e5238575a685e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/696k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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    },
    {
      "cell_type": "code",
      "source": [
        "generator = pipeline(\"text-generation\",model=model,tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "-sSiMF5x10Rz"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set_seed(42)"
      ],
      "metadata": {
        "id": "90yQTypH5mvZ"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_text= \"COVID-19 is\""
      ],
      "metadata": {
        "id": "C8uvWlZGOtY_"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator(input_text, max_length=100, num_return_sequences=5, do_sample=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGhHiL5RO27A",
        "outputId": "2a168c3c-bf10-4074-b41d-9d433118852e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'COVID-19 is still unclear.'},\n",
              " {'generated_text': 'COVID-19 is a recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).'},\n",
              " {'generated_text': 'COVID-19 is associated with the highest number of deaths globally, in the United States.'},\n",
              " {'generated_text': 'COVID-19 is a leading public health threat, resulting in a series of clinical illnesses ranging from common cold to severe acute respiratory syndrome (SARS).'},\n",
              " {'generated_text': 'COVID-19 is a fast-tracking epidemic and now has a mortality rate of 30%.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_text= \"\"\"question:  Can you eat acorn squash peel?\n",
        "               context: The acorn squash peel is incredibly nutritious, in addition to the fibers that help in intestinal health, it contains valuable antioxidant substances for health\n",
        "               answer: the answer to the question given the context is  \"\"\""
      ],
      "metadata": {
        "id": "BQGK3oJJ5_Nk"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator(input_text , max_length=200, num_return_sequences=1, do_sample=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2wBCX3n55tI",
        "outputId": "dddb96ca-7a04-4c65-d84f-cb844b9ab1f5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'question:  Can you eat acorn squash peel?\\n               context: The acorn squash peel is incredibly nutritious, in addition to the fibers that help in intestinal health, it contains valuable antioxidant substances for health\\n               answer: the answer to the question given the context is  : Can you eat acorn squash peel?'}]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_text= \"\"\" question: what were identified in the cranial cavity?\n",
        "                context: A brain mass and a spinal cord were identified in the cranial cavity and the vertebral canal\n",
        "                answer: the answer to the question given the context is \"\"\""
      ],
      "metadata": {
        "id": "q9JtFLDP7ZK9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator(input_text , max_length=200, num_return_sequences=1, do_sample=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8m4qY6e8m_d",
        "outputId": "8b0e5e9c-62ca-4f03-b5c1-e70b38e01c2b"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': ' question: what were identified in the cranial cavity?\\n                context: A brain mass and a spinal cord were identified in the cranial cavity and the vertebral canal\\n                answer: the answer to the question given the context is : a brain mass and a spinal cord.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BioGptTokenizer, BioGptForCausalLM\n",
        "tokenizer = BioGptTokenizer.from_pretrained(\"microsoft/biogpt\")\n",
        "model = BioGptForCausalLM.from_pretrained(\"microsoft/biogpt\")\n"
      ],
      "metadata": {
        "id": "NfiTyYGoHb90",
        "outputId": "57074128-c319-444b-c903-051c5bcf2198",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346,
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            "bb1777940cc04e829261a50220b5e53b",
            "fcc92aab859840b4a712195862d15c89",
            "8a316f25a0bb40b28c8a860e5ed88a2f",
            "fc58ad0170de45569cfb1852df13e229",
            "afd34ccdebd040609ea4f759e4208e5a",
            "c9919cd3e8544b4c94cf4b3027f82063",
            "c6665045d64f460e9d2b8e441569d5e9",
            "9433b381bab0409a9c854e0f0ce453ad",
            "6b67d8aa432c476bae503667244fe6ae",
            "4eb0f30c0904476b9dcc52b0a8b5be80",
            "9afbed5fca25461589a22da584931b5f",
            "367905ee7f094cb0899a3637f4770887",
            "5acfe984cfd7420a8bd851c13460c114",
            "21b71d8b469446879ffaa2d5116e16db",
            "bfa5af11fcbf495c816bb53c2380fc3e",
            "44807e51e99549d79eeaa825b2596d40",
            "32e445556d5347efb7c0f3d87ec5c64e",
            "0e82ab1cdb554599b4728a19cdb083d1",
            "4fca10e89c0945e8bc05dd5084c904c4"
          ]
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/927k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd44427f182745e58dae1668d3ffde56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/696k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dee6fb9c80a48bea473813a5c0a64db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29e63eb5ca83403fabe4bfdbb2b0f896"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.56G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b67d8aa432c476bae503667244fe6ae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-d854a35f3e8e>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mencoded_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mencoded_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mlast_hidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_hidden_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mone\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlast_hidden_states\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'CausalLMOutputWithCrossAttentions' object has no attribute 'last_hidden_state'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Can you eat acorn squash peel?\"\n",
        "encoded_input = tokenizer(text, return_tensors='pt')\n",
        "output = model(**encoded_input)"
      ],
      "metadata": {
        "id": "1T-YJtCERuB5"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(output[0].size())\n",
        "one=output[0][0][-1]"
      ],
      "metadata": {
        "id": "Afln8mXsQ6nh",
        "outputId": "84e743d3-9d7c-4841-dab9-54ac46ef82f7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([1, 11, 42384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(\"The acorn squash peel is incredibly nutritious, in addition to the fibers that help in intestinal health, it contains valuable antioxidant substances for health\", return_tensors=\"pt\")\n",
        "outputs = model(**inputs)\n",
        "\n",
        "two=outputs [0][0][-1]\n",
        "print(two.size())"
      ],
      "metadata": {
        "id": "FkmKn95EpvCr",
        "outputId": "bbf4b684-08d9-4554-b066-04d35f5569c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([42384])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "cos = torch.nn.CosineSimilarity(dim=0)\n",
        "output = cos(one, two)\n",
        "\n",
        "# display the output tensor\n",
        "print(\"Cosine Similarity:\",output)"
      ],
      "metadata": {
        "id": "ZORGwECJqKwU",
        "outputId": "2eb54639-caca-4b94-9709-9d277b0593b3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cosine Similarity: tensor(0.6654, grad_fn=<SumBackward1>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tensor2 = torch.tensor([0.13, 0.23, 2.33, 0.45])\n",
        "print(tensor2.size())"
      ],
      "metadata": {
        "id": "FTvkKnTyqZ49",
        "outputId": "2bf7454a-3547-4266-a6e4-9d9dee298c6b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([4])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(one[:10])"
      ],
      "metadata": {
        "id": "bU2j-1k8rQdd",
        "outputId": "562fe3f4-b048-493a-9c2b-6c89bab1e896",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([-0.1223,  0.0990, -0.9851,  0.4618, -1.1176, -0.6136,  0.1432, -0.4271,\n",
            "        -0.6509,  0.3859], grad_fn=<SliceBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(two[:10])"
      ],
      "metadata": {
        "id": "1Uji9UVfsKDV",
        "outputId": "57555342-7ef2-4afe-d22a-abda3bc70662",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([-0.1223,  0.0990, -0.9851,  0.4618, -1.1176, -0.6136,  0.1432, -0.4271,\n",
            "        -0.6509,  0.3859], grad_fn=<SliceBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r9rgvMsGsMcm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}