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+ " 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[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m61.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
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+ " Downloading safetensors-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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+ "\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",
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+ "\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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+ "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
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+ "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",
1438
+ "Collecting huggingface-hub<1.0,>=0.16.4 (from transformers)\n",
1439
+ " Downloading huggingface_hub-0.17.3-py3-none-any.whl (295 kB)\n",
1440
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m31.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
1441
+ "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.0)\n",
1442
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
1443
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
1444
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
1445
+ "Installing collected packages: safetensors, huggingface-hub, tokenizers, transformers\n",
1446
+ "Successfully installed huggingface-hub-0.17.3 safetensors-0.4.0 tokenizers-0.14.1 transformers-4.34.1\n"
1447
+ ]
1448
+ }
1449
+ ],
1450
+ "source": [
1451
+ "# Install stadard library\n",
1452
+ "! pip install --upgrade transformers"
1453
+ ]
1454
+ },
1455
+ {
1456
+ "cell_type": "code",
1457
+ "source": [
1458
+ "from transformers import pipeline, RobertaTokenizer, AutoModel"
1459
+ ],
1460
+ "metadata": {
1461
+ "id": "vG5ndbr_rYjL"
1462
+ },
1463
+ "execution_count": 2,
1464
+ "outputs": []
1465
+ },
1466
+ {
1467
+ "cell_type": "code",
1468
+ "source": [
1469
+ "# Initialise the tokenizer\n",
1470
+ "tokenizer = RobertaTokenizer.from_pretrained(\"NaturalAntibody/human_heavy\", return_tensors=\"pt\")"
1471
+ ],
1472
+ "metadata": {
1473
+ "id": "1GNqH8HlrzmF",
1474
+ "colab": {
1475
+ "base_uri": "https://localhost:8080/",
1476
+ "height": 113,
1477
+ "referenced_widgets": [
1478
+ "153f591849c9469f9c7d75d7e9b634f1",
1479
+ "dc1205fdf40943afa4c38474bc4f64f1",
1480
+ "6b088e9c6123437eb0038a28ef1ba3f4",
1481
+ "fe10ba9faced4b4eb39411c0c8b2459f",
1482
+ "d540b77369024b3ba57094d3cbfa61f5",
1483
+ "0034b6163dfe471ba866b76804a2bf15",
1484
+ "9713aad631444f6aaf5fce62a1dbd066",
1485
+ "c6c34f93295844fb8de76bbf6287049f",
1486
+ "0c8bc6126406443d9fb94be194de7ab2",
1487
+ "1867baf7efb341f8a003db34fe76a7a8",
1488
+ "9bd859bbc6884f0fbb2ce1a4d9367567",
1489
+ "05b4dbc202f246959ae9ff20c4ef3c47",
1490
+ "f83cde870013407998417621191fc300",
1491
+ "515a3d33853046d6bab4970957ac567d",
1492
+ "25a1b910af6849d2aff6cd637021abf7",
1493
+ "0bbf4fc7ad434ce8824b2e1a5db2e9e6",
1494
+ "9aeeb7473318489f8edcd9096e0ab9c8",
1495
+ "c1d538e67c2143ae838b47d6234c35e4",
1496
+ "c6fc7129129d42808b4ad86c425bfdc2",
1497
+ "6a822ad5365b4cafb09671018e2c89ce",
1498
+ "f40dac28264748a6b060b1f8a2f93e8e",
1499
+ "4f75ca4a4ee5478a9a0b4fa3515d9883",
1500
+ "7f9968268eca49f4b419f8bd45c994ca",
1501
+ "e9cb4647acd942cb9afb5218ae520ccd",
1502
+ "a85e9c58c93341d79f305d0b6a39b55d",
1503
+ "3ce15f4a41da46a2b1b44227610135ad",
1504
+ "e7779803673140ab9ec006cdd51507c4",
1505
+ "82b47b9c74c446fbb207be42b2968fe5",
1506
+ "b56f6a4df2c54a578009739453cb4114",
1507
+ "4623c92a88674544adf316511c91b5db",
1508
+ "98def8ff926e49df912e858c47dd77d7",
1509
+ "d8a53d3004c041aaa02ab36cd5fd5a9e",
1510
+ "b59b45ee951449e381d380068e723725"
1511
+ ]
1512
+ },
1513
+ "outputId": "8780582a-7ac4-41d0-9e20-6ae33aa7c6bb"
1514
+ },
1515
+ "execution_count": 3,
1516
+ "outputs": [
1517
+ {
1518
+ "output_type": "display_data",
1519
+ "data": {
1520
+ "text/plain": [
1521
+ "Downloading (…)olve/main/vocab.json: 0%| | 0.00/285 [00:00<?, ?B/s]"
1522
+ ],
1523
+ "application/vnd.jupyter.widget-view+json": {
1524
+ "version_major": 2,
1525
+ "version_minor": 0,
1526
+ "model_id": "153f591849c9469f9c7d75d7e9b634f1"
1527
+ }
1528
+ },
1529
+ "metadata": {}
1530
+ },
1531
+ {
1532
+ "output_type": "display_data",
1533
+ "data": {
1534
+ "text/plain": [
1535
+ "Downloading (…)olve/main/merges.txt: 0.00B [00:00, ?B/s]"
1536
+ ],
1537
+ "application/vnd.jupyter.widget-view+json": {
1538
+ "version_major": 2,
1539
+ "version_minor": 0,
1540
+ "model_id": "05b4dbc202f246959ae9ff20c4ef3c47"
1541
+ }
1542
+ },
1543
+ "metadata": {}
1544
+ },
1545
+ {
1546
+ "output_type": "display_data",
1547
+ "data": {
1548
+ "text/plain": [
1549
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/633 [00:00<?, ?B/s]"
1550
+ ],
1551
+ "application/vnd.jupyter.widget-view+json": {
1552
+ "version_major": 2,
1553
+ "version_minor": 0,
1554
+ "model_id": "7f9968268eca49f4b419f8bd45c994ca"
1555
+ }
1556
+ },
1557
+ "metadata": {}
1558
+ }
1559
+ ]
1560
+ },
1561
+ {
1562
+ "cell_type": "code",
1563
+ "source": [
1564
+ "# Initialise model\n",
1565
+ "unmasker = pipeline('fill-mask', model=\"NaturalAntibody/human_heavy\", tokenizer=tokenizer, top_k=20 )"
1566
+ ],
1567
+ "metadata": {
1568
+ "id": "3CYcwIOU3xCY",
1569
+ "colab": {
1570
+ "base_uri": "https://localhost:8080/",
1571
+ "height": 49,
1572
+ "referenced_widgets": [
1573
+ "c0aa7032982c463093101e487f2d3e71",
1574
+ "6920441e232940a4a608eb7577706799",
1575
+ "1808e1cbda074aee9e1f57a791c609b6",
1576
+ "a5726f1999134a6789f4907b07877834",
1577
+ "f6d353a4804c470c8a100242f187e8de",
1578
+ "ee86dc93ab16424b87de4885c2e1bda1",
1579
+ "7326188c5ac6436da7ae9c86d71764a3",
1580
+ "552a3e3bb28c46f7a0bde9ddc2766a74",
1581
+ "ff89842849794ed58534a9e10cfa71bb",
1582
+ "db0726115c9347f4bc98e75b34261da2",
1583
+ "c29ce5aabb4a4116835609249958f82e"
1584
+ ]
1585
+ },
1586
+ "outputId": "d79ba6ef-d775-4746-80b0-6c75567f492f"
1587
+ },
1588
+ "execution_count": 4,
1589
+ "outputs": [
1590
+ {
1591
+ "output_type": "display_data",
1592
+ "data": {
1593
+ "text/plain": [
1594
+ "Downloading pytorch_model.bin: 0%| | 0.00/57.9M [00:00<?, ?B/s]"
1595
+ ],
1596
+ "application/vnd.jupyter.widget-view+json": {
1597
+ "version_major": 2,
1598
+ "version_minor": 0,
1599
+ "model_id": "c0aa7032982c463093101e487f2d3e71"
1600
+ }
1601
+ },
1602
+ "metadata": {}
1603
+ }
1604
+ ]
1605
+ },
1606
+ {
1607
+ "cell_type": "code",
1608
+ "source": [
1609
+ "# Predict the residue probability at one or more masked positions\n",
1610
+ "# mark position to predict with '<mask>'\n",
1611
+ "seq = \"QLVSGPEVKKPGASVKVSCKASGYIFNNYGISWVRQAPGQGLEWMGWISTDNGNTNYAQKVQGRVTMTTDTSTSTAYMELRSLRYDDTAVYYC<mask>ATNWGSYFEHWGQGTLVTVSS\"\n",
1612
+ "\n",
1613
+ "residueProbability = unmasker(seq)\n",
1614
+ "\n",
1615
+ "# Print residue probabilities\n",
1616
+ "for scores in residueProbability:\n",
1617
+ " print(f\"Amino Acid : {scores['token_str']}, probability = {scores['score']}\")"
1618
+ ],
1619
+ "metadata": {
1620
+ "colab": {
1621
+ "base_uri": "https://localhost:8080/"
1622
+ },
1623
+ "id": "6rtUxgbYsygY",
1624
+ "outputId": "86deb631-11ce-4ca9-e89a-b4ec2d497751"
1625
+ },
1626
+ "execution_count": 5,
1627
+ "outputs": [
1628
+ {
1629
+ "output_type": "stream",
1630
+ "name": "stdout",
1631
+ "text": [
1632
+ "Amino Acid : A, probability = 0.9447903037071228\n",
1633
+ "Amino Acid : V, probability = 0.018683552742004395\n",
1634
+ "Amino Acid : T, probability = 0.01454206183552742\n",
1635
+ "Amino Acid : G, probability = 0.01338210329413414\n",
1636
+ "Amino Acid : S, probability = 0.002618547063320875\n",
1637
+ "Amino Acid : R, probability = 0.0010079601779580116\n",
1638
+ "Amino Acid : D, probability = 0.0009759279782883823\n",
1639
+ "Amino Acid : I, probability = 0.0006436451803892851\n",
1640
+ "Amino Acid : P, probability = 0.0005739639163948596\n",
1641
+ "Amino Acid : L, probability = 0.0005292189307510853\n",
1642
+ "Amino Acid : E, probability = 0.0004481423238757998\n",
1643
+ "Amino Acid : M, probability = 0.00041029718704521656\n",
1644
+ "Amino Acid : C, probability = 0.00034322161809541285\n",
1645
+ "Amino Acid : F, probability = 0.0002548334014136344\n",
1646
+ "Amino Acid : W, probability = 0.00019698048708960414\n",
1647
+ "Amino Acid : Y, probability = 0.00015392513887491077\n",
1648
+ "Amino Acid : H, probability = 0.00014831082080490887\n",
1649
+ "Amino Acid : N, probability = 0.00013396698341239244\n",
1650
+ "Amino Acid : K, probability = 0.00011472667392808944\n",
1651
+ "Amino Acid : Q, probability = 2.202932591899298e-05\n"
1652
+ ]
1653
+ }
1654
+ ]
1655
+ }
1656
+ ]
1657
+ }