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      "cell_type": "markdown",
      "source": [
        "# Human Heavy Example\n",
        "\n",
        "Here we present human heavy chain 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"
      }
    },
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxL4QKeNqYXI",
        "outputId": "27336d4b-c5f6-4c5f-bbb0-43e6b2c24a29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.34.1-py3-none-any.whl (7.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m26.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.4)\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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m47.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
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            "Collecting huggingface-hub<1.0,>=0.16.4 (from transformers)\n",
            "  Downloading huggingface_hub-0.17.3-py3-none-any.whl (295 kB)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "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": {
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "# Initialise the tokenizer\n",
        "tokenizer = RobertaTokenizer.from_pretrained(\"NaturalAntibody/human_heavy\", return_tensors=\"pt\")"
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        "id": "1GNqH8HlrzmF",
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      "source": [
        "# Initialise model\n",
        "unmasker = pipeline('fill-mask', model=\"NaturalAntibody/human_heavy\", tokenizer=tokenizer, top_k=20 )"
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        {
          "output_type": "display_data",
          "data": {
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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/"
        },
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        "outputId": "86deb631-11ce-4ca9-e89a-b4ec2d497751"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Amino Acid : A, probability = 0.9447903037071228\n",
            "Amino Acid : V, probability = 0.018683552742004395\n",
            "Amino Acid : T, probability = 0.01454206183552742\n",
            "Amino Acid : G, probability = 0.01338210329413414\n",
            "Amino Acid : S, probability = 0.002618547063320875\n",
            "Amino Acid : R, probability = 0.0010079601779580116\n",
            "Amino Acid : D, probability = 0.0009759279782883823\n",
            "Amino Acid : I, probability = 0.0006436451803892851\n",
            "Amino Acid : P, probability = 0.0005739639163948596\n",
            "Amino Acid : L, probability = 0.0005292189307510853\n",
            "Amino Acid : E, probability = 0.0004481423238757998\n",
            "Amino Acid : M, probability = 0.00041029718704521656\n",
            "Amino Acid : C, probability = 0.00034322161809541285\n",
            "Amino Acid : F, probability = 0.0002548334014136344\n",
            "Amino Acid : W, probability = 0.00019698048708960414\n",
            "Amino Acid : Y, probability = 0.00015392513887491077\n",
            "Amino Acid : H, probability = 0.00014831082080490887\n",
            "Amino Acid : N, probability = 0.00013396698341239244\n",
            "Amino Acid : K, probability = 0.00011472667392808944\n",
            "Amino Acid : Q, probability = 2.202932591899298e-05\n"
          ]
        }
      ]
    }
  ]
}