notebooks/vocab_example.ipynb

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        "<a href=\"https://colab.research.google.com/github/matthewchung74/blogs/blob/main/vocab_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
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          "metadata": {},
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      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIO9MpGAoH4w"
      },
      "source": [
        "Install the Transformers and Datasets libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "kF21FhdSoH4w"
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      "outputs": [],
      "source": [
        "!pip -qq install datasets transformers[sentencepiece]"
      ]
    },
    {
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          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
        {
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              "tokenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
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            ]
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        }
      ],
      "source": [
        "from transformers import BertTokenizerFast\n",
        "\n",
        "tokenizer = BertTokenizerFast.from_pretrained(\n",
        "  'huggingface-course/bert-base-uncased-tokenizer-without-normalizer'\n",
        ")"
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            "text/plain": [
              "Downloading readme:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
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            "text/plain": [
              "Generating train split:   0%|          | 0/182822 [00:00<?, ? examples/s]"
            ]
          },
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        {
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            "text/plain": [
              "Generating test split:   0%|          | 0/6150 [00:00<?, ? examples/s]"
            ]
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        {
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            "text/plain": [
              "Generating validation split:   0%|          | 0/4183 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'question', 'opa', 'opb', 'opc', 'opd', 'cop', 'choice_type', 'exp', 'subject_name', 'topic_name'],\n",
              "    num_rows: 182822\n",
              "})"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset('medmcqa', split='train').shuffle(seed=42)\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ka-ZVy7rR7CS",
        "outputId": "04042559-8731-4c54-9ac7-93572c676731"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'OPV can be used if vaccine l monitor is showing?. Average gain of height in first year ?'"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\". \".join(dataset['question'][0:2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
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        },
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        "outputId": "837c0fc0-cbaf-4cf9-f734-d5c5968d3ff0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['[UNK]', 'can', 'be', 'used', 'if', 'vaccine', 'l', 'monitor', 'is', 'showing', '?', '.', '[UNK]', 'gain', 'of', 'height', 'in', 'first', 'year', '?']\n"
          ]
        }
      ],
      "source": [
        "text = \". \".join(dataset['question'][0:2])\n",
        "print(tokenizer.tokenize(text))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['[UNK]', '[UNK]', ',', '[UNK]', ',', '2', '[UNK]', ',', '[UNK]', 'and', '[UNK]', ',', '[UNK]', '[UNK]', '[UNK]', '[UNK]', '[UNK]', '[UNK]', ':', '[UNK]', '.', '2']\n"
          ]
        }
      ],
      "source": [
        "text = \"RADIOLOGIC EXAMINATION, CHEST, 2 VIEWS, PA and LATERAL,CHEST XRAY PA AND LATERAL HISTORY: R06.2\"\n",
        "print(tokenizer.tokenize(text))"
      ]
    }
  ],
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