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    {
      "cell_type": "markdown",
      "source": [
        "# nanoBERT Example\n",
        "\n",
        "Here we present nanoBERT, a nanobody-specific transformer. Its primary application is positing infilling, predicting what amino acids could be available at a given position according to the nanobody-specific distribution.  "
      ],
      "metadata": {
        "id": "JU2dnhr24egK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxL4QKeNqYXI",
        "outputId": "168466b8-c470-4fab-d656-fbaece49200b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.34.1-py3-none-any.whl (7.7 MB)\n",
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            "Collecting tokenizers<0.15,>=0.14 (from transformers)\n",
            "  Downloading tokenizers-0.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "Collecting huggingface-hub<1.0,>=0.16.4 (from transformers)\n",
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            "Installing collected packages: safetensors, huggingface-hub, tokenizers, transformers\n",
            "Successfully installed huggingface-hub-0.17.3 safetensors-0.4.0 tokenizers-0.14.1 transformers-4.34.1\n"
          ]
        }
      ],
      "source": [
        "# Install stadard library\n",
        "! pip install --upgrade transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, RobertaTokenizer, AutoModel"
      ],
      "metadata": {
        "id": "vG5ndbr_rYjL"
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      "execution_count": 2,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Initialise the tokenizer\n",
        "tokenizer = RobertaTokenizer.from_pretrained(\"NaturalAntibody/nanoBERT\", return_tensors=\"pt\")"
      ],
      "metadata": {
        "id": "1GNqH8HlrzmF",
        "colab": {
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        {
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      "cell_type": "code",
      "source": [
        "# Initialise model\n",
        "unmasker = pipeline('fill-mask', model=\"NaturalAntibody/nanoBERT\", tokenizer=tokenizer, top_k=20 )"
      ],
      "metadata": {
        "id": "3CYcwIOU3xCY",
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        {
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          "data": {
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              "Downloading pytorch_model.bin:   0%|          | 0.00/57.9M [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "# Predict the residue probability at one or more masked positions\n",
        "# mark position to predict with '<mask>'\n",
        "seq = \"QLVSGPEVKKPGASVKVSCKASGYIFNNYGISWVRQAPGQGLEWMGWISTDNGNTNYAQKVQGRVTMTTDTSTSTAYMELRSLRYDDTAVYYC<mask>ATNWGSYFEHWGQGTLVTVSS\"\n",
        "\n",
        "residueProbability = unmasker(seq)\n",
        "\n",
        "# Print residue probabilities\n",
        "for scores in residueProbability:\n",
        "    print(f\"Amino Acid : {scores['token_str']}, probability = {scores['score']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rtUxgbYsygY",
        "outputId": "6f340ae8-8285-418f-f41b-fd8f5016bce9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Amino Acid : T, probability = 0.28031080961227417\n",
            "Amino Acid : A, probability = 0.23146940767765045\n",
            "Amino Acid : K, probability = 0.13046956062316895\n",
            "Amino Acid : N, probability = 0.12727046012878418\n",
            "Amino Acid : V, probability = 0.06813304871320724\n",
            "Amino Acid : S, probability = 0.040362704545259476\n",
            "Amino Acid : R, probability = 0.02661576308310032\n",
            "Amino Acid : G, probability = 0.02190057747066021\n",
            "Amino Acid : I, probability = 0.011736654676496983\n",
            "Amino Acid : H, probability = 0.011689980514347553\n",
            "Amino Acid : Y, probability = 0.010142817161977291\n",
            "Amino Acid : E, probability = 0.007347866427153349\n",
            "Amino Acid : M, probability = 0.0065687913447618484\n",
            "Amino Acid : L, probability = 0.00589130399748683\n",
            "Amino Acid : P, probability = 0.0055612483993172646\n",
            "Amino Acid : Q, probability = 0.004960155114531517\n",
            "Amino Acid : D, probability = 0.003958782181143761\n",
            "Amino Acid : F, probability = 0.0026921925600618124\n",
            "Amino Acid : C, probability = 0.0016993506578728557\n",
            "Amino Acid : W, probability = 0.0009979679016396403\n"
          ]
        }
      ]
    }
  ]
}