File size: 6,847 Bytes
bccbe1d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"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": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gxL4QKeNqYXI",
"outputId": "256d9b91-ed93-462a-8d6f-8c257b973f91"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.34.1)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.4)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.17.3)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
"Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.1)\n",
"Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.0)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n"
]
}
],
"source": [
"# Install stadard library\n",
"! pip install --upgrade transformers"
]
},
{
"cell_type": "code",
"source": [
"from transformers import pipeline, RobertaTokenizer, AutoModel"
],
"metadata": {
"id": "vG5ndbr_rYjL"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Initialise the tokenizer\n",
"tokenizer = RobertaTokenizer.from_pretrained(\"NaturalAntibody/nanoBERT\", return_tensors=\"pt\")"
],
"metadata": {
"id": "1GNqH8HlrzmF"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Initialise model\n",
"unmasker = pipeline('fill-mask', model=\"tadsatlawa/nanoBERT\", tokenizer=tokenizer, top_k=20 )"
],
"metadata": {
"id": "3CYcwIOU3xCY"
},
"execution_count": 12,
"outputs": []
},
{
"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": "da127f6a-e076-44ba-fce8-ff68c06cf354"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Amino Acid : S, probability = 0.4827525019645691\n",
"Amino Acid : A, probability = 0.22524100542068481\n",
"Amino Acid : N, probability = 0.09490441530942917\n",
"Amino Acid : Y, probability = 0.07571367919445038\n",
"Amino Acid : K, probability = 0.04161035269498825\n",
"Amino Acid : T, probability = 0.027568845078349113\n",
"Amino Acid : H, probability = 0.009884347207844257\n",
"Amino Acid : C, probability = 0.008951968513429165\n",
"Amino Acid : V, probability = 0.007528781425207853\n",
"Amino Acid : R, probability = 0.006156255956739187\n",
"Amino Acid : G, probability = 0.005135924089699984\n",
"Amino Acid : I, probability = 0.004699127282947302\n",
"Amino Acid : W, probability = 0.0030531329102814198\n",
"Amino Acid : M, probability = 0.0022762243170291185\n",
"Amino Acid : F, probability = 0.001321254065260291\n",
"Amino Acid : E, probability = 0.0009838981786742806\n",
"Amino Acid : L, probability = 0.0006674979231320322\n",
"Amino Acid : D, probability = 0.000666878477204591\n",
"Amino Acid : Q, probability = 0.0005539602716453373\n",
"Amino Acid : P, probability = 0.00032376404851675034\n"
]
}
]
}
]
} |