Ch2_Tokenizers_to_Putting_it_all_together.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/JonathanSum/Hugging-Face-Course/blob/main/Ch2_Tokenizers_to_Putting_it_all_together.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "! pip install datasets transformers[sentencepiece]"
      ],
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        {
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          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-1.10.2-py3-none-any.whl (542 kB)\n",
            "\u001b[K     |████████████████████████████████| 542 kB 6.2 MB/s \n",
            "\u001b[?25hCollecting transformers[sentencepiece]\n",
            "  Downloading transformers-4.9.1-py3-none-any.whl (2.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6 MB 46.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.19.5)\n",
            "Collecting fsspec>=2021.05.0\n",
            "  Downloading fsspec-2021.7.0-py3-none-any.whl (118 kB)\n",
            "\u001b[K     |████████████████████████████████| 118 kB 72.2 MB/s \n",
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            "Installing collected packages: tqdm, tokenizers, sacremoses, pyyaml, huggingface-hub, xxhash, transformers, sentencepiece, fsspec, datasets\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.41.1\n",
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            "Successfully installed datasets-1.10.2 fsspec-2021.7.0 huggingface-hub-0.0.12 pyyaml-5.4.1 sacremoses-0.0.45 sentencepiece-0.1.91 tokenizers-0.10.3 tqdm-4.61.2 transformers-4.9.1 xxhash-2.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZG13qoZpe6J"
      },
      "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)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7ZM1zzmpiUt",
        "outputId": "be7e01de-d2fb-41ed-b059-2636c505c59f"
      },
      "source": [
        "tokenized_text = \"Jim Henson was a puppeteer\".split()\n",
        "print(tokenized_text)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Jim', 'Henson', 'was', 'a', 'puppeteer']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ok5_V2bppkz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "daffea5e2e4947e8b160031352c45fb5",
            "acc383169464401d9d5898792a699b57",
            "d4bd137d6f80448da9ee6ace61e75b8a",
            "b882373328814ad19ca04f61c35a0fa2",
            "32afe58b21364851b998b3ec0f980255",
            "4524bf31519c4580a5ed5171619b47f9",
            "d3f022d9a42f46ad8c6e6ac9361b73a9",
            "1bf8dbb984bc474190c65a4aba532ce7",
            "2afeab9c6f7c4fe69a5c6d1836993af3",
            "435b68d7abed4c1fa7bed19673ec789a",
            "11e73058c2c94fc68a9a02390ed38380",
            "d7b8717d188b43eda48d77dc197260db",
            "d72400cdca8848e59f2fa943ebc28bfd",
            "7057f176abbd4b0aa1fc6d8e6acaa0da",
            "f8817df2f8154c5fb6b4ec0761daa5c0",
            "ead0fb776ed1480bb55100b7742723e6",
            "6655365cdaa4496ead9c7a68281b6810",
            "20e602e2a5174c4e9a85cf5224127c9f",
            "eda03ff3f6bd4934bbfcbef8cf45845a",
            "1ecef945b68f44cb8c4c48025215a123",
            "bf4ab1e19a984235952f8baf63e3d942",
            "3b3dced5e147453daf8ca876ab5fb3e7",
            "2c75642197c74be2ba6b650139c0ed7a",
            "9039480835a4413ea0c9bb79c68e346f",
            "daaa200c4a4540509cfa438bc2dd46c3",
            "abe971f0febd4d998cf7702045da8016",
            "0badc8bdc5b94d909205637c0d12d1c4",
            "94fe210b884343a395c19bac2d0cdaf5",
            "49d6f23eb8474cb9a29eacef12a1cc10",
            "54d5268707114cefbdddd87b56050fa0",
            "a2a62e59114a4e58af7a2742dfc01d1a",
            "60cef3fefa1e4b1f826e68e49c1d70b6",
            "c93e468773864492ad2c7c1338a28dbf",
            "cee7b06cd8624ba691b4973d1e655578",
            "b7b204fda475444fb87154995f26e559",
            "6caa71185b564eb2bb1e1a51159d4853",
            "1cdc72299e4946fa875a94a42b8f5950",
            "ef1007a0fd0548c980d0d50abe9d69eb",
            "ee23c3e89fff444c9eb90aafdc7804cf",
            "2a9b0e1073ca4e08af2f8142dd0c043c",
            "a4fa1631bcc641cd993f2374e6d3aa7c",
            "7fc279818cf346e48eceb77ffa01908a",
            "f10aeb5d91be471d9e5d988d052518c3",
            "e6ad980a7d964761a092fce6eef59b68"
          ]
        },
        "outputId": "2a33ab75-bc21-452f-c93a-5448873b31b0"
      },
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "daffea5e2e4947e8b160031352c45fb5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d7b8717d188b43eda48d77dc197260db",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c75642197c74be2ba6b650139c0ed7a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cee7b06cd8624ba691b4973d1e655578",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h57dUFrefRVh"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAmTcpOyfs8c",
        "outputId": "f1f207e5-8691-4a79-9904-01c003833b90"
      },
      "source": [
        "tokenizer(\"Using a Transformer network is simple\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 7993, 170, 13809, 23763, 2443, 1110, 3014, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kNm-l0Omf4rq",
        "outputId": "e78a718d-80b8-4cef-cc5d-8ae07847b5ed"
      },
      "source": [
        "tokenizer.save_pretrained(\"directory_on_my_computer\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('directory_on_my_computer/tokenizer_config.json',\n",
              " 'directory_on_my_computer/special_tokens_map.json',\n",
              " 'directory_on_my_computer/vocab.txt',\n",
              " 'directory_on_my_computer/added_tokens.json',\n",
              " 'directory_on_my_computer/tokenizer.json')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBDP_pwA0n_c",
        "outputId": "f2058b7c-d5b0-4a0a-e0ee-0ce99fc2ecc2"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "sequence = \"Transformer is great.\"\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "\n",
        "print(tokens)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Trans', '##former', 'is', 'great', '.']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sbxXe0zb1PmW",
        "outputId": "85d848be-3fb1-4bde-b1bb-3647c9f9a81a"
      },
      "source": [
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(ids)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[13809, 23763, 1110, 1632, 119]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fwyvShQS118r"
      },
      "source": [
        "Try1:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKNOeMeg11Rk",
        "outputId": "291b19bc-f9fe-4fe5-e789-60a23b7c2d5c"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "sequence = \"I’ve been waiting for a HuggingFace course my whole life.\"\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "\n",
        "print(tokens)\n",
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(ids)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['I', '’', 've', 'been', 'waiting', 'for', 'a', 'Hu', '##gging', '##F', '##ace', 'course', 'my', 'whole', 'life', '.']\n",
            "[146, 787, 1396, 1151, 2613, 1111, 170, 20164, 10932, 2271, 7954, 1736, 1139, 2006, 1297, 119]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVqm3cxs17xu"
      },
      "source": [
        "Try2:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTgs69Uo18u6",
        "outputId": "5ba392e3-b8c3-406b-f706-1f9199f56bff"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "sequence = \"I hate this so much!\"\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "\n",
        "print(tokens)\n",
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "print(ids)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['I', 'hate', 'this', 'so', 'much', '!']\n",
            "[146, 4819, 1142, 1177, 1277, 106]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HL2GCyj62OSW",
        "outputId": "f4f2375d-befb-46cc-b7e6-cd141ee07213"
      },
      "source": [
        "decoded_string = tokenizer.decode([13809, 23763, 1110, 1632, 119])\n",
        "print(decoded_string)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transformer is great.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQUYt5BPBpSM"
      },
      "source": [
        "Handling multiple sequences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "zPMxj6HJBpt-",
        "outputId": "25006b73-9c0c-416f-cb0c-58865c67d1ad"
      },
      "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",
        "\n",
        "print(input_ids.shape)\n",
        "# This line will fail.\n",
        "model(input_ids)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([14])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-5ccecfd785f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# This line will fail.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\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/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/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    630\u001b[0m             \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m             \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 632\u001b[0;31m             \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\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    633\u001b[0m         )\n\u001b[1;32m    634\u001b[0m         \u001b[0mhidden_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistilbert_output\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# (bs, seq_len, dim)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/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    486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minputs_embeds\u001b[0m \u001b[0;32mis\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--> 488\u001b[0;31m             \u001b[0minputs_embeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# (bs, seq_length, dim)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m         return self.transformer(\n\u001b[1;32m    490\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs_embeds\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/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1049\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1050\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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   1052\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\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;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/distilbert/modeling_distilbert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids)\u001b[0m\n\u001b[1;32m    111\u001b[0m         embeddings)\n\u001b[1;32m    112\u001b[0m         \"\"\"\n\u001b[0;32m--> 113\u001b[0;31m         \u001b[0mseq_length\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;36m1\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    114\u001b[0m         \u001b[0mposition_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# (max_seq_length)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0mposition_ids\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mposition_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# (bs, max_seq_length)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: Dimension out of range (expected to be in range of [-1, 0], but got 1)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxM4oHPtDnXN",
        "outputId": "0f28d4ed-4cb2-47a4-9975-5de01f3edb69"
      },
      "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)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "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=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ru-_-xTsGGiT",
        "outputId": "cc588c16-d2cf-4d92-d8df-6667a1311f68"
      },
      "source": [
        "batched_ids = [ids, ids]\n",
        "\n",
        "print(\"Input IDs:\", torch.tensor(batched_ids))\n",
        "\n",
        "output = model(torch.tensor(batched_ids))\n",
        "print(\"Logits:\", output.logits)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input IDs: tensor([[ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,\n",
            "          2026,  2878,  2166,  1012],\n",
            "        [ 1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,\n",
            "          2026,  2878,  2166,  1012]])\n",
            "Logits: tensor([[-2.7276,  2.8789],\n",
            "        [-2.7276,  2.8789]], grad_fn=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTaJQcN6Gf7U"
      },
      "source": [
        "padding_id = 100\n",
        "\n",
        "batched_ids = [\n",
        "  [200, 200, 200],\n",
        "  [200, 200, padding_id]\n",
        "]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vARhsNwkMicE",
        "outputId": "1ff6b4d2-dd38-4c53-ab10-9f2d5f522a6e"
      },
      "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)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[ 1.5694, -1.3895],\n",
            "        [ 0.5803, -0.4125]], grad_fn=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mbPCIBLQmYf"
      },
      "source": [
        "✏️ Try it out! Apply the tokenization manually on the two sentences used in section 2 (“I’ve been waiting for a HuggingFace course my whole life.” and “I hate this so much!”). Pass them through the model and check that you get the same logits as in section 2. Now batch them together using the padding token, then create the proper attention mask. Check that you obtain the same results when going through the model!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrVBe99dQSM_",
        "outputId": "383d9760-9f7b-4701-fa30-19df95666bdc"
      },
      "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 hate this so much!\"\n",
        "sequence2 = \"I’ve been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "tokens1 = tokenizer.tokenize(sequence1)\n",
        "ids1 = tokenizer.convert_tokens_to_ids(tokens1)\n",
        "\n",
        "tokens2 = tokenizer.tokenize(sequence2)\n",
        "ids2 = tokenizer.convert_tokens_to_ids(tokens2)\n",
        "\n",
        "input_ids1 = torch.tensor([ids1])\n",
        "print(\"Input1 IDs:\", input_ids1)\n",
        "print(input_ids1.size())\n",
        "input_ids2 = torch.tensor([ids2])\n",
        "print(\"Input2 IDs:\", input_ids2)\n",
        "print(input_ids2.size())\n"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input1 IDs: tensor([[1045, 5223, 2023, 2061, 2172,  999]])\n",
            "torch.Size([1, 6])\n",
            "Input2 IDs: tensor([[ 1045,  1521,  2310,  2042,  3403,  2005,  1037, 17662, 12172,  2607,\n",
            "          2026,  2878,  2166,  1012]])\n",
            "torch.Size([1, 14])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5B6_llqYp_H",
        "outputId": "22cbec55-030a-44fe-baf9-ca7952000ff3"
      },
      "source": [
        "torch.cat((input_ids1,input_ids1))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1045, 5223, 2023, 2061, 2172,  999],\n",
              "        [1045, 5223, 2023, 2061, 2172,  999]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EmK0nyYMYQL1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQ9gvhu4XmYf",
        "outputId": "55f2317a-234b-4f44-ec43-e19cbee7887a"
      },
      "source": [
        "input_ids1 = torch.tensor([ids1])\n",
        "print(\"Input1 IDs:\", input_ids1)\n",
        "\n",
        "output1 = model(input_ids1)\n",
        "print(\"Logits:\", output1.logits)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input1 IDs: tensor([[1045, 5223, 2023, 2061, 2172,  999]])\n",
            "Logits: tensor([[ 3.1931, -2.6685]], grad_fn=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZZkppcmWe86",
        "outputId": "39d559eb-e12b-4e84-c48e-a0076e59454d"
      },
      "source": [
        "ids1+[padding_id]*8"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1045, 5223, 2023, 2061, 2172, 999, 100, 100, 100, 100, 100, 100, 100, 100]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Qhn_psxXOpV",
        "outputId": "88d3de12-8081-4010-e99b-a019af987cce"
      },
      "source": [
        "ids2"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1045,\n",
              " 1521,\n",
              " 2310,\n",
              " 2042,\n",
              " 3403,\n",
              " 2005,\n",
              " 1037,\n",
              " 17662,\n",
              " 12172,\n",
              " 2607,\n",
              " 2026,\n",
              " 2878,\n",
              " 2166,\n",
              " 1012]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYzOrraLZr_3"
      },
      "source": [
        "batched_ids_l = [\n",
        "    ids1+[padding_id]*8,\n",
        "    ids2\n",
        "]\n",
        "\n"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNNNGsI6T-R3",
        "outputId": "ede62811-8723-4812-f473-b3e68841ae01"
      },
      "source": [
        "attention_mask = [\n",
        "  [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
        "  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
        "]\n",
        "\n",
        "outputs = model(torch.tensor(batched_ids_l), attention_mask=torch.tensor(attention_mask))\n",
        "print(outputs.logits)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[ 3.1931, -2.6685],\n",
            "        [-2.5720,  2.6852]], grad_fn=<AddmmBackward>)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9k0iVTAdqoj"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITAgZVEzdqqK"
      },
      "source": [
        "sequence = sequence[:max_sequence_length]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60uOaXl4aXbu"
      },
      "source": [
        "Cooment. We have to pad. And most importantly, we have to make the ids's attention mask's certain elements to be zero because they are padding."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DQ8d3sNdPhe"
      },
      "source": [
        "**Putting it all together**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "eab9ed63a45c4c7fb565566d2757a624",
            "ea8c1fe272af4c29a62f7a3a512c8342",
            "c7b150e785cf4368ae62fd6eb09c25f1",
            "dbd7b0ce94f44a11a26b40181bde7c3d",
            "b0824875dda74a169e711c9b497d6ae6",
            "8735cb1fc5914e2d99a2d864b89f7064",
            "8a8899ece29742cf936d1f8edca85c8e",
            "cb5108ccde76412dbb0f745765701231",
            "21d57088ac4447bbaccd84a3ffc09a42",
            "c40927479cb64e618e5586192669cf7b",
            "b5b223990aa14757846e9960498451b1",
            "984149089fe2448789857baa6e293c78",
            "2a94dc0ba4a14f6d9723ddb858a3a2f4",
            "f8a775d0438b4e21a1964a1b4e180d7b",
            "b2f0baad05c54e58b803ab41fa533f8c",
            "4f31302130c0414093edf8ac2eaf5003",
            "8ea7e776d51b4815b1485b9a540a77c2",
            "3c7d703e2d2e4956bc4067b067bf1d6c",
            "030879bcc45b458daa995be937da7432",
            "8d89572d6af44989806942c88a9a8dd2",
            "33738f9b758b477887cac7b8aeede068",
            "40f2f042bd7a40d3a249e22ef4550bba",
            "0299c48b779a482b90fd6363207e3e1e",
            "a469d1428df54a54b9e3b55d757921e7",
            "816ee9f18a0941bd88a332172002e33f",
            "2c2e9185e6804220ba2b94d3bcef736e",
            "61a08f25a75340a1ac39ab9ad089a310",
            "0679aab26aac4eb5a88681890a50e986",
            "f92238e9796f49918337666a7ce02d00",
            "9b0440cb1d2d4e6686ffefc882ea1c44",
            "b980ce8085434a0b844e83670b616616",
            "a681e38c8ad94092bc29d306c906392c",
            "4fa5ddeefa1549cd805831a020178daf"
          ]
        },
        "id": "ldndIwmRdRvJ",
        "outputId": "6a9b4719-1987-49ba-de4e-54f312eda32a"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "model_inputs = tokenizer(sequence)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eab9ed63a45c4c7fb565566d2757a624",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "984149089fe2448789857baa6e293c78",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0299c48b779a482b90fd6363207e3e1e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kc6vpHZOdrr_"
      },
      "source": [
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "model_inputs = tokenizer(sequence)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpB7-js8emaP"
      },
      "source": [
        "sequences = [\n",
        "  \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "  \"So have I!\"\n",
        "]\n",
        "\n",
        "model_inputs = tokenizer(sequences)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwHy72EfeuqC"
      },
      "source": [
        "model_inputs = tokenizer(sequences, padding=\"longest\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABhwhWu5euri"
      },
      "source": [
        "# Will pad the sequences up to the maximum sequence length\n",
        "model_inputs = tokenizer(sequences, padding=\"longest\")\n",
        "\n",
        "# Will pad the sequences up to the model max length\n",
        "# (512 for BERT or DistilBERT)\n",
        "model_inputs = tokenizer(sequences, padding=\"max_length\")\n",
        "\n",
        "# Will pad the sequences up to the specified max length\n",
        "model_inputs = tokenizer(sequences, padding=\"max_length\", max_length=8)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQZCUwJAfd0C"
      },
      "source": [
        "sequences = [\n",
        "  \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "  \"So have I!\"\n",
        "]\n",
        "\n",
        "# Will truncate the sequences that are longer than the model max length\n",
        "# (512 for BERT or DistilBERT)\n",
        "model_inputs = tokenizer(sequences, truncation=True)\n",
        "\n",
        "# Will truncate the sequences that are longer than the specified max length\n",
        "model_inputs = tokenizer(sequences, max_length=8, truncation=True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNSm7FoVkVkp"
      },
      "source": [
        "sequences = [\n",
        "  \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "  \"So have I!\"\n",
        "]\n",
        "\n",
        "# Returns PyTorch tensors\n",
        "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"pt\")\n",
        "\n",
        "# Returns TensorFlow tensors\n",
        "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"tf\")\n",
        "\n",
        "# Returns NumPy arrays\n",
        "model_inputs = tokenizer(sequences, padding=True, return_tensors=\"np\")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roQbizNHkYHu",
        "outputId": "f43d8fdb-b77a-4c07-e33a-213a16754085"
      },
      "source": [
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "model_inputs = tokenizer(sequence)\n",
        "model_inputs"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_GiZ9t2knQT",
        "outputId": "178b444b-8eac-4693-eb78-4f54a8ca8d12"
      },
      "source": [
        "tokenizer.tokenize(sequence)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i',\n",
              " \"'\",\n",
              " 've',\n",
              " 'been',\n",
              " 'waiting',\n",
              " 'for',\n",
              " 'a',\n",
              " 'hugging',\n",
              " '##face',\n",
              " 'course',\n",
              " 'my',\n",
              " 'whole',\n",
              " 'life',\n",
              " '.']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zl9HJ02JlwKz",
        "outputId": "c7ea2563-3f69-4da4-ad81-766b813dc7c1"
      },
      "source": [
        "sequence = \"I've been waiting for a HuggingFace course my whole life.\"\n",
        "\n",
        "model_inputs = tokenizer(sequence)\n",
        "print(model_inputs[\"input_ids\"])\n",
        "\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "print(ids)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[101, 1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012, 102]\n",
            "[1045, 1005, 2310, 2042, 3403, 2005, 1037, 17662, 12172, 2607, 2026, 2878, 2166, 1012]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJtr8_EPlwMO",
        "outputId": "d37f38f0-73a9-4b1e-db0f-64b25a7bca12"
      },
      "source": [
        "print(tokenizer.decode(model_inputs[\"input_ids\"]))\n",
        "print(tokenizer.decode(ids))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[CLS] i've been waiting for a huggingface course my whole life. [SEP]\n",
            "i've been waiting for a huggingface course my whole life.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "53a37b16598840e5885ab272cfa9bc03",
            "a7c7259e6c0c48d7929916372a563598",
            "11fe225de7df42109e785cad6a85c5e3",
            "56604770dfc5476da5d7744cf8ea33f1",
            "ad18ed046ca54c9e97907f394dba767e",
            "eb1a39d80ee6453baba48195d01bf768",
            "e3b466869be44b73b843e427b3e32f55",
            "18bd2575b84d445cbc1cc343ebcf543b",
            "6eda9c859ff444cf814b4e1dd49fd887",
            "198f7a9386964875932687fe8f1db553",
            "6d808c9d28aa44f9bb77c74772554bcd"
          ]
        },
        "id": "HS_9P5zaom9o",
        "outputId": "2f31914e-b360-4430-94ed-af226ce7a6ba"
      },
      "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 = [\n",
        "  \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "  \"So have I!\"\n",
        "]\n",
        "\n",
        "tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "tokens"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "53a37b16598840e5885ab272cfa9bc03",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
              "          2607,  2026,  2878,  2166,  1012,   102],\n",
              "        [  101,  2061,  2031,  1045,   999,   102,     0,     0,     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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVQD2DUGoyuj",
        "outputId": "16d651d7-6803-4a1a-886d-db30f1da5dae"
      },
      "source": [
        "tokens[\"input_ids\"].shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 16])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gQ0uPzporf0"
      },
      "source": [
        "output = model(**tokens)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VB-bGasWpNL9",
        "outputId": "beda4ab8-cc6b-444f-e3f5-d572443510a1"
      },
      "source": [
        "output"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SequenceClassifierOutput([('logits', tensor([[-1.5607,  1.6123],\n",
              "                                   [-3.6183,  3.9137]], grad_fn=<AddmmBackward>))])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    }
  ]
}