Behind_the_pipeline_(PyTorch).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_x8Ch0uy0IFP"
      },
      "source": [
        "# Behind the pipeline (PyTorch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMmTPtN30IFR"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HzQgoHbb0IFS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "409f1e4d-6ac7-4211-9944-35d0cbb9dda1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/510.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.7/510.5 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/510.5 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.3/510.5 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m481.3/510.5 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "HNAcUH_40IFT",
        "outputId": "38a659c9-e1d8-4aac-9c4d-c7921ad4b263",
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            "2a371aebda354cb985dc0b65fdf40407"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
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            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
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              "model_id": "a140c8fa11db4dbe8e8f28956361cc1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_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": "5f43bfcd42d041aba270f3db57bfdf08"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "330958fab5a34a77b97c8de7be5c9ecc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9598048329353333},\n",
              " {'label': 'NEGATIVE', 'score': 0.9994558691978455}]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\")\n",
        "classifier(\n",
        "    [\n",
        "        \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "        \"I hate this so much!\",\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "K5q1e5NV0IFU",
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        "outputId": "7c9c5852-15bd-4a0f-daa8-9e7e1579234b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_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": "86e6ff37802948e18c1686e050c478e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5ab79ccb88d4459ba3cb854b7a40c5f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "932833043dc04444b020f656fda1658b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4YJdJL_e0IFU",
        "outputId": "dc7d468f-792b-4e9b-aaf0-0a983f03356f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
            "          2607,  2026,  2878,  2166,  1012,   102],\n",
            "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
            "        [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
          ]
        }
      ],
      "source": [
        "raw_inputs = [\n",
        "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "    \"I hate this so much!\",\n",
        "]\n",
        "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "print(inputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JxMwnpxS0IFV"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModel\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "model = AutoModel.from_pretrained(checkpoint)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4XeLzO8b0IFV",
        "outputId": "9d922c9f-74d6-4bb1-d0ce-c3e7f00fd565",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 16, 768])\n"
          ]
        }
      ],
      "source": [
        "outputs = model(**inputs)\n",
        "print(outputs.last_hidden_state.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HJZG1p6u0IFV"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "outputs = model(**inputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lUe0t9e50IFW",
        "outputId": "c877b010-4dfa-45c7-e45b-4e7e5aba8ebe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 2])\n"
          ]
        }
      ],
      "source": [
        "print(outputs.logits.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Egr9LamR0IFW",
        "outputId": "894c0967-5f3d-4668-89e8-7aa8ced7ac0b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-1.5607,  1.6123],\n",
            "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ],
      "source": [
        "print(outputs.logits)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8_U9f1Ib0IFW",
        "outputId": "37f164db-e0bc-4190-c695-25fff57d1c71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[4.0195e-02, 9.5980e-01],\n",
            "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward0>)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
        "print(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3d0EHUM40IFW",
        "outputId": "c4428fa8-f4c7-4ace-f779-1f39320f5f81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'NEGATIVE', 1: 'POSITIVE'}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "model.config.id2label"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HzApo9qo22GI"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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