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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hjiang13/CodeBERT/blob/master/NLP_course_Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section we’ll take a closer look at creating and using a model."
      ],
      "metadata": {
        "id": "O7AdnsZlQYQW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating a Transformer"
      ],
      "metadata": {
        "id": "buYEBi_GQe8I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a model from the default configuration initializes it with random values:"
      ],
      "metadata": {
        "id": "u_vkSMVkRn_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertConfig, BertModel\n",
        "\n",
        "# Building the config\n",
        "config = BertConfig()\n",
        "\n",
        "# Building the model from the config\n",
        "model = BertModel(config)"
      ],
      "metadata": {
        "id": "4Ik3n62dQavZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "print(config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sj_Oi1R7Qp3L",
        "outputId": "b72dbf27-6f50-4ecc-d21c-245f5388d1a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BertConfig {\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.38.2\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading a Transformer model that is already trained is simple — we can do this using the from_pretrained() method:"
      ],
      "metadata": {
        "id": "AIcDgtzGRp19"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertModel\n",
        "\n",
        "model = BertModel.from_pretrained(\"bert-base-cased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "aDucA0JbRNmM",
        "outputId": "70b297c7-afc5-47b6-d6f7-2fb13ff39dff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27239ec6ff884619993546ac65a0b0e0"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving methods\n",
        "Saving a model is as easy as loading one — we use the save_pretrained() method, which is analogous to the from_pretrained() method:"
      ],
      "metadata": {
        "id": "I37v9p1iSH_-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"directory_on_my_computer\")"
      ],
      "metadata": {
        "id": "ULMtvFWMRvjM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ls directory_on_my_computer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69yAVfkpSW9c",
        "outputId": "adc1abf0-3815-4a34-ddfe-4ea7f51b0d22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "config.json  model.safetensors\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat directory_on_my_computer/config.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bycLCI6ySY_d",
        "outputId": "bda61f16-fa61-4603-97da-765f733d5d6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"_name_or_path\": \"bert-base-cased\",\n",
            "  \"architectures\": [\n",
            "    \"BertModel\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
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            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.38.2\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 28996\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "L8XYlTc-Si8U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using a Transformer model for inference\n"
      ],
      "metadata": {
        "id": "Ubx5M7JrSqNg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = [\"Hello!\", \"Cool.\", \"Nice!\"]"
      ],
      "metadata": {
        "id": "a3MwYTVvSxyK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tokenizer\n",
        "# Loading and Saving\n",
        "Loading and saving tokenizers is as simple as it is with models"
      ],
      "metadata": {
        "id": "iC4l7y_7Ty0J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "96026e4e2ab64fc58ae7fd853857ebd1",
            "59a029eaeee94d199c40049618d5b94c",
            "e908eaa0f47e4e2cab369acb4e9df5bc",
            "3dc2fb9e339a400689924bb6b6f5bd4d",
            "a9d3102ead314c749d33e02cf223ec55",
            "3cb23d7e7d35441690e67ffc31adc782",
            "6454862db516428e8101d8bd555ee431",
            "8bdd02547fd148aa8c58af7ba0ab61c0",
            "e7193834babf4adaa3aa7fac646851c9",
            "621aa60f07f74a369adb771c652bc971",
            "22b1dae928524d2a89a71a88087082eb",
            "f354f920fd734def974adaf0c9b5f9e1",
            "00f2a2a3596a40f080a3ea31624bde28",
            "04f70d476a97434791bdf501ca19542a",
            "aca474961f5f47bfa1c357e221edc47e",
            "08a45c57f07f40cea80c80bd4402c0c0",
            "be559cca4d4d4031ad5a239cd71738d1",
            "afa187752653448aa1bfb322b127a1bf",
            "947066889ff243238c20ae417c6b124e",
            "6f6ef87ff7184fd3a7a1cb3b1c348db8",
            "c645963a6137425490ddc930c1b83611",
            "4f0f5f05b393447497eae180b8b419b5",
            "60394179084a4cf9afd5c19d3916ce00",
            "0a4de46ac6c3405f8772b416c2ec2236",
            "de420d2031a341309a2a9e5cb10685f1",
            "eacb3b448250498da1ae3d8f76822b86",
            "7eb709c97a2d4b3cadc64121c4d39943",
            "f7db090792fe49e5863265b246c71ac5",
            "66aa12d1229041e8b45efbfafd2a57a6",
            "b90790fef73745cbad409e9c8a45d1f7",
            "07ad33847cbb4bdba7ee3d5175b5b1ac",
            "3c9643e0822b4cb4a8238e0d62514415",
            "57d405044dde47168f311694984adcee"
          ]
        },
        "id": "4pBxPsBPT8ag",
        "outputId": "aff64f4b-40a1-4917-c298-f32a42739e1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96026e4e2ab64fc58ae7fd853857ebd1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f354f920fd734def974adaf0c9b5f9e1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60394179084a4cf9afd5c19d3916ce00"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.tokenize(sequences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "cIyK0quHVDLQ",
        "outputId": "b8e97343-3c46-42a6-8b74-794da985a572"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "unhashable type: 'list'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-da079b5f4b18>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msequences\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py\u001b[0m in \u001b[0;36mtokenize\u001b[0;34m(self, text, **kwargs)\u001b[0m\n\u001b[1;32m    583\u001b[0m         \u001b[0;31m# [\"This is something\", \"<special_token_1>\", \"  else\"]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 585\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mno_split_token\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    586\u001b[0m                 \u001b[0mtok_extended\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_added_tokens_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_added_tokens_encoder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    587\u001b[0m                 \u001b[0mleft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokens\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Encoding\n",
        "Translating text to numbers is known as encoding. Encoding is done in a two-step process: the tokenization, followed by the conversion to input IDs."
      ],
      "metadata": {
        "id": "Oh-mCbs1UouY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_sequences = tokenizer(sequences)"
      ],
      "metadata": {
        "id": "v7aYyc7XUBuQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(encoded_sequences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuHrybtYUTV8",
        "outputId": "cd9b8f20-2aac-40b0-c8db-4e799554d0a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': [[101, 8667, 106, 102], [101, 13297, 119, 102], [101, 8835, 106, 102]], 'token_type_ids': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "model_inputs = torch.tensor(encoded_sequences)\n"
      ],
      "metadata": {
        "id": "HM3TKMS2TCXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output = model(model_inputs)"
      ],
      "metadata": {
        "id": "mYQeLo1hTHy2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOB0HOROTKnj",
        "outputId": "d080ee6c-1c19-4da7-c24b-a58658422d8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=tensor([[[ 4.4496e-01,  4.8276e-01,  2.7797e-01,  ..., -5.4032e-02,\n",
            "           3.9393e-01, -9.4770e-02],\n",
            "         [ 2.4943e-01, -4.4093e-01,  8.1772e-01,  ..., -3.1917e-01,\n",
            "           2.2992e-01, -4.1172e-02],\n",
            "         [ 1.3668e-01,  2.2518e-01,  1.4502e-01,  ..., -4.6915e-02,\n",
            "           2.8224e-01,  7.5566e-02],\n",
            "         [ 1.1789e+00,  1.6738e-01, -1.8187e-01,  ...,  2.4671e-01,\n",
            "           1.0441e+00, -6.1974e-03]],\n",
            "\n",
            "        [[ 3.6436e-01,  3.2464e-02,  2.0258e-01,  ...,  6.0110e-02,\n",
            "           3.2451e-01, -2.0996e-02],\n",
            "         [ 7.1866e-01, -4.8725e-01,  5.1740e-01,  ..., -4.4012e-01,\n",
            "           1.4553e-01, -3.7545e-02],\n",
            "         [ 3.3223e-01, -2.3271e-01,  9.4876e-02,  ..., -2.5268e-01,\n",
            "           3.2172e-01,  8.1076e-04],\n",
            "         [ 1.2523e+00,  3.5754e-01, -5.1321e-02,  ..., -3.7840e-01,\n",
            "           1.0526e+00, -5.6255e-01]],\n",
            "\n",
            "        [[ 2.4042e-01,  1.4718e-01,  1.2110e-01,  ...,  7.6062e-02,\n",
            "           3.3564e-01,  2.8262e-01],\n",
            "         [ 6.5701e-01, -3.2787e-01,  2.4968e-01,  ..., -2.5920e-01,\n",
            "           2.0175e-01,  3.3275e-01],\n",
            "         [ 2.0160e-01,  1.5783e-01,  9.8969e-03,  ..., -3.8850e-01,\n",
            "           4.1307e-01,  3.9732e-01],\n",
            "         [ 1.0175e+00,  6.4387e-01, -7.8147e-01,  ..., -4.2109e-01,\n",
            "           1.0925e+00, -4.8456e-02]]], grad_fn=<NativeLayerNormBackward0>), pooler_output=tensor([[-0.6856,  0.5262,  1.0000,  ...,  1.0000, -0.6112,  0.9971],\n",
            "        [-0.6055,  0.4997,  0.9998,  ...,  0.9999, -0.6753,  0.9769],\n",
            "        [-0.7702,  0.5447,  0.9999,  ...,  1.0000, -0.4655,  0.9894]],\n",
            "       grad_fn=<TanhBackward0>), hidden_states=None, past_key_values=None, attentions=None, cross_attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Handling multiple sequences"
      ],
      "metadata": {
        "id": "j8U5b_QwbPsP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "\n",
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "input_ids = torch.tensor(ids)\n",
        "# This line will fail.\n",
        "model(input_ids)"
      ],
      "metadata": {
        "id": "5nASwJAvbN_E",
        "outputId": "183dbfb1-9413-4836-dfb3-51cf9c546388",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608,
          "referenced_widgets": [
            "3242a85043aa4fa7ba68d0919f4daf8a",
            "164858c018144322a68b5e6e80dff20f",
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            "991d1c7e16da425782acc6afbe77c26f",
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            "0f7904fe28a8458e85609bfa3bf680d0",
            "eea4029b11f345068ee2e74d6001fc87",
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            "149de113be0847188bbc2d3211de5bf5",
            "a64a0e4f87524210b868805918f109b5",
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            "e9892815289444f7af91b406384ff003",
            "02d156bdfb614d75ac0211b63a6165ef",
            "c5cb25e85a6c48a68f095a8831816d62",
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            "be5472686a2b4cf996d0a562707f8ea8",
            "6f413735f13842178c431d9321b468c5",
            "074a0c2699864bf5acab9db823284bea",
            "d52e9eb1be874b6a9c70aae3eca2cb5b",
            "cd8348cc9d5c431b94c057882b8e9d2b",
            "3cfc634276c2400e95b43c5b35d697e9",
            "22df4771a43a4015b30364d19900d5e7",
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            "42e0d15eb10d4cc485b5c15760a90bef",
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            "6d792e607811421f8929913729e87533",
            "d5e6aefc8f0141adba3b460dd7a376eb",
            "06496b33a2e74909a9ffdbadb53e516a",
            "34918123c7d5464f919de854d4114598",
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            "af58a95121984a06b814911a4bab02f9",
            "1b33b2ebd7f240ada85d8afb79df1175",
            "95b12f1c62b14f1f8e52c10644ac0520",
            "0dd07abf710c4129ba5750d72f18cdba",
            "6114ab507a63476c9b59c373268c0408",
            "e96f51244f5547ae83437c58b24848d4",
            "df8daface9f34fe4854fd9edbdd2e2f4",
            "9f6e45e4ea6d4cb7b9cb98b03849febe",
            "fc392293f27347299f4db7cf260d45f3",
            "19b00ec1571f4a9299624305d0a57caa"
          ]
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "3242a85043aa4fa7ba68d0919f4daf8a"
            }
          },
          "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": "0e8e9f96cf1c440cb55b491c57a8cb95"
            }
          },
          "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": "d52e9eb1be874b6a9c70aae3eca2cb5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "897fda4af12f4ae78f65cf892572573a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "too many indices for tensor of dimension 1",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-2b9fc1b6cd40>\u001b[0m in \u001b[0;36m<cell line: 14>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0minput_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# This line will fail.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1510\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1511\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1518\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1519\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1521\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1522\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/distilbert/modeling_distilbert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1000\u001b[0m         \u001b[0mreturn_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_dict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_return_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1001\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1002\u001b[0;31m         distilbert_output = self.distilbert(\n\u001b[0m\u001b[1;32m   1003\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1004\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1510\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1511\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1518\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1519\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1521\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1522\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/distilbert/modeling_distilbert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    800\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"You cannot specify both input_ids and inputs_embeds at the same time\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    801\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0minput_ids\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 802\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn_if_padding_and_no_attention_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    803\u001b[0m             \u001b[0minput_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0minputs_embeds\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mwarn_if_padding_and_no_attention_mask\u001b[0;34m(self, input_ids, attention_mask)\u001b[0m\n\u001b[1;32m   4167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4168\u001b[0m         \u001b[0;31m# Check only the first and last input IDs to reduce overhead.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4169\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4170\u001b[0m             warn_string = (\n\u001b[1;32m   4171\u001b[0m                 \u001b[0;34m\"We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: too many indices for tensor of dimension 1"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_inputs = tokenizer(sequence, return_tensors=\"pt\")\n",
        "print(tokenized_inputs[\"input_ids\"])"
      ],
      "metadata": {
        "id": "ODd08awvbW2i",
        "outputId": "c497cef1-7c30-43fa-d46c-7a94c81cfd4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
            "          2607,  2026,  2878,  2166,  1012,   102]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "\n",
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "input_ids = torch.tensor([ids])\n",
        "print(\"Input IDs:\", input_ids)\n",
        "\n",
        "output = model(input_ids)\n",
        "print(\"Logits:\", output.logits)"
      ],
      "metadata": {
        "id": "hRipK4KybgOZ",
        "outputId": "9a19c67b-262c-4b01-933b-afb2eedfc050",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input IDs: tensor([[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,\n",
            "          2026,  2878,  2166,  1012]])\n",
            "Logits: tensor([[-2.7276,  2.8789]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Batching is the act of sending multiple sentences through the model, all at once. If you only have one sentence, you can just build a batch with a single sequence:"
      ],
      "metadata": {
        "id": "Ys5m4ciLbq08"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There’s a second issue, though. When you’re trying to batch together two (or more) sentences, they might be of different lengths. If you’ve ever worked with tensors before, you know that they need to be of rectangular shape, so you won’t be able to convert the list of input IDs into a tensor directly. To work around this problem, we usually pad the inputs."
      ],
      "metadata": {
        "id": "4CYnpUWVb7sB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "\n",
        "sequence1_ids = [[200, 200, 200]]\n",
        "sequence2_ids = [[200, 200]]\n",
        "batched_ids = [\n",
        "    [200, 200, 200],\n",
        "    [200, 200, tokenizer.pad_token_id],\n",
        "]\n",
        "\n",
        "print(model(torch.tensor(sequence1_ids)).logits)\n",
        "print(model(torch.tensor(sequence2_ids)).logits)\n",
        "print(model(torch.tensor(batched_ids)).logits)"
      ],
      "metadata": {
        "id": "UnQ2rl8Hb8kQ",
        "outputId": "726472bf-c6f0-4a08-98a5-b92c39cc96df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 1.5694, -1.3895]], grad_fn=<AddmmBackward0>)\n",
            "tensor([[ 0.5803, -0.4125]], grad_fn=<AddmmBackward0>)\n",
            "tensor([[ 1.5694, -1.3895],\n",
            "        [ 1.3374, -1.2163]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batched_ids = [\n",
        "    [200, 200, 200],\n",
        "    [200, 200, tokenizer.pad_token_id],\n",
        "]\n",
        "\n",
        "attention_mask = [\n",
        "    [1, 1, 1],\n",
        "    [1, 1, 0],\n",
        "]\n",
        "\n",
        "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
        "print(outputs.logits)"
      ],
      "metadata": {
        "id": "KcULr9lMcKZT",
        "outputId": "076c4594-3d17-4349-f278-b04b4d88861e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 1.5694, -1.3895],\n",
            "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "\n",
        "sequence1 = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "sequence2 = \"I hate this so much!\"\n",
        "\n",
        "tokens1 = tokenizer.tokenize(sequence1)\n",
        "ids1 = tokenizer.convert_tokens_to_ids(tokens1)\n",
        "\n",
        "input_ids1 = torch.tensor([ids1])\n",
        "print(\"Input IDs1:\", input_ids1)\n",
        "\n",
        "output1 = model(input_ids1)\n",
        "print(\"Logits1:\", output1.logits)\n",
        "\n",
        "tokens2 = tokenizer.tokenize(sequence2)\n",
        "ids2 = tokenizer.convert_tokens_to_ids(tokens2)\n",
        "\n",
        "input_ids2 = torch.tensor([ids2])\n",
        "print(\"Input IDs2:\", input_ids2)\n",
        "\n",
        "output2 = model(input_ids2)\n",
        "print(\"Logits2:\", output2.logits)"
      ],
      "metadata": {
        "id": "_nvuvuH8cWCq",
        "outputId": "d6f14e90-b69e-447f-9f3b-1860abf47e7e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input IDs1: tensor([[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,\n",
            "          2026,  2878,  2166,  1012]])\n",
            "Logits1: tensor([[-2.7276,  2.8789]], grad_fn=<AddmmBackward0>)\n",
            "Input IDs2: tensor([[1045, 5223, 2023, 2061, 2172,  999]])\n",
            "Logits2: tensor([[ 3.1931, -2.6685]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batched_ids = [\n",
        "    [ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012],\n",
        "    [1045, 5223, 2023, 2061, 2172,  999, tokenizer.pad_token_id , tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.pad_token_id],\n",
        "]\n",
        "attention_mask = [\n",
        "    [ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607, 2026,  2878,  2166,  1012],\n",
        "    [1045, 5223, 2023, 2061, 2172,  999, 0 , 0, 0, 0, 0, 0, 0, 0],\n",
        "]\n",
        "outputs = model(torch.tensor(batched_ids), attention_mask=torch.tensor(attention_mask))\n",
        "print(outputs.logits)"
      ],
      "metadata": {
        "id": "hP-1E517c7Ua",
        "outputId": "cb5b3ec4-77d2-4085-8e61-c5816279dd62",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-2.7276,  2.8789],\n",
            "        [ 3.1931, -2.6685]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Wrapping up"
      ],
      "metadata": {
        "id": "fviUPc62l-eM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "sequences = [\"I've been waiting for a HuggingFace course my whole life.\", \"So have I!\"]\n",
        "\n",
        "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "output = model(**tokens)"
      ],
      "metadata": {
        "id": "N_z8aGvodhwi"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(output)"
      ],
      "metadata": {
        "id": "fqWPr8AdmB8Z",
        "outputId": "6f737018-a692-4e73-f47d-a6561668dc99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SequenceClassifierOutput(loss=None, logits=tensor([[-1.5607,  1.6123],\n",
            "        [-3.6183,  3.9137]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7AevJf5RmFQ1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}