{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "QS0v2bceN4Or" }, "source": [ "Builds a database of vector embeddings from list of abstracts" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "l5RwcIG8OAjX" }, "source": [ "## Some Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sfwT5YW2JCnu", "outputId": "bf3a4179-f81c-44ca-b2d6-447dda960c80" }, "outputs": [], "source": [ "!pip install transformers==4.28.0\n", "!pip install -U sentence-transformers\n", "!pip install datasets\n", "!pip install langchain\n", "!pip install torch\n", "!pip install faiss-cpu" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "psoTvOp4VkBE" }, "outputs": [], "source": [ "import os\n", "import shutil\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from tqdm.auto import tqdm\n", "import torch" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "arZiN8QRHS_a" }, "outputs": [], "source": [ "import locale\n", "locale.getpreferredencoding = lambda: \"UTF-8\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 209, "referenced_widgets": [ "4756793562464107802ca63258cdd741", "e64b509fcaa140dca388a942581fa88b", "9a78d50cb9f64f858fb2f78fd1235967", "e2f1551939d64a96a727ec6a83011b14", "01631fd683614bba8fc2e1cbb852a903", "0c6c75568b8f4375ba3cc33f0bbdf18a", "32e19b6cf34d46dbb502a90396834c5e", "467335b5d972451c858e81a6b25edd11", "8ab268be33ba435f983cddb2d837a777", "1b42f23411214c7e97e57a2ea3b7141b", "b5250321ce5f475a8d68367d022655a1", "5319f5e5881c4d57948cc46e8be6e0c4", "170411958f9e425590920120b83bf61f", "030bf3f0c6884385a3ffa72f4d6d8ba2", "c778e6a0d4024e0cae5f599230ac5722", "95a2dfb7693143cc8ad4385fb2b50bcf", "5f36bea5a55e43acafe87ea1b023c2ee", "6f62a3f27a3540cdb5835f8962f48fde", "1e38b3954c9f45b08436295506167ced", "dc5c0590ed98485098aef13d7801b24d", "89a533c45558444788f89a45716108ed", "311594051ad340f0913bc433dce2bd3c", "9d917ea7069148758ac6199d381be06c", "38ec3ad4add441c7b7f0cb958252ec58", "b645063cce184086beef6c096fa3c44b", "91e570fd4c0a4837914f132704586557", "1d9a2628b67944899e5338f049273da1", "0797a8351a60486db96fa1a048552d58", "72ee250083cf4dcbbf1deb5dbdc103c4", "18f6dec14da34ddcb3cb98276d63cb72", "538efc104df346f3bf91a49eca94367c", "2dfe10ce19fd4ac2bb94f0372d1d10f6", "997929d0430e4496b788f5bd23b8c4e7", "ea8432e454df4beb838e300e8bdb56c0", "5e2d7a0adf224f8989437795687cf20e", "cf55e8e10bdc4629954b17274d485520", "5fed7204ce474f9fae16d8808ae1c727", "1d79770b67b74034b1a3ac8a37809bb2", "3dbb17a9a7b5401b964434a69cd94f87", "6f065208f4944d82a6551f0ae40ed2c3", "f0ecdcb30c144111abca01c26db2c6ca", "05530b88c9484897858671c9b722423a", "8c0b7ae76a4a4f10a8d8270224d89c8b", "3dfafe2393f247d7a70a6e002a6854b7", "389eae5b28914487b225afaa4dc0d43a", "c3c92a853085430baa28d56f8dafe95a", "41f0f37cc72844729277b2c1b9cc6d35", "a8d6e71dbf10406499c68def3599439b", "d7d4231b1c8a485d8d2958face49cea2", "2a74124601a742f89588f8ffa780e7e7", "41a7b7307d7a4848b8dc862a28b00b2f", "a073802ac2f24043b8b57a8df4895adf", "72d0038bcabf45c58e4572d3e9f152cc", "a6ab044e9506468c812bece3f9b88b69", "f76e6bd96ae54be49ee52427d06a9a2c", "d46bace56f3742b1a1c15c76ab3e8ec6", "38b0fe57793947819f14a9e25670dd78", "6c1754813ade4d508e6635116c7ce630", "649d6e51d6424490ad5f17e41bfb9524", "4107201c49eb4e5180c4019635f23db4", "5c826a3ba1744d2b9069e0a2b91fe709", "c60edf4a38114654b8e4041f0a0b8dfa", "3ecf941bc53a4a4bb8b897590705486f", "97630ae2964449e48868175ac1de6aac", "8e19141b16f44aec831b7f6c35ab0cea", "2a3efb6398594f989bfdebf495f60ffb" ] }, "id": "JwWs0-Uu6ohg", "outputId": "254e0e81-5b3e-492d-bd5d-5f6cabb6df81" }, "outputs": [], "source": [ "from transformers import AutoTokenizer, BertForSequenceClassification\n", "\n", "m_tokenizer = AutoTokenizer.from_pretrained(\"biodatlab/MIReAD-Neuro\")\n", "m_model = BertForSequenceClassification.from_pretrained(\"biodatlab/MIReAD-Neuro\")\n", "miread_bundle = (m_tokenizer,m_model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BR-adEUUz9su" }, "outputs": [], "source": [ "def create_lbert_embed(sents,bundle):\n", " tokenizer = bundle[0]\n", " model = bundle[1]\n", " model.cuda()\n", " tokens = tokenizer(sents,padding=True,truncation=True,return_tensors='pt')\n", " device = torch.device('cuda')\n", " tokens = tokens.to(device)\n", " with torch.no_grad():\n", " embeds = model(**tokens, output_hidden_states=True,return_dict=True).pooler_output\n", " return embeds.cpu()\n", "\n", "def create_miread_embed(sents,bundle):\n", " tokenizer = bundle[0]\n", " model = bundle[1]\n", " model.cuda()\n", " tokens = tokenizer(sents,\n", " max_length=512,\n", " padding=True,\n", " truncation=True,\n", " return_tensors=\"pt\"\n", " )\n", " device = torch.device('cuda')\n", " tokens = tokens.to(device)\n", " with torch.no_grad():\n", " out = model.bert(**tokens)\n", " feature = out.last_hidden_state[:, 0, :]\n", " return feature.cpu()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 426, "referenced_widgets": [ "fdbeedf5a9e24131bc5978e38e4a993a", "fb5d46ba519348e19b709896f29fd080", "972708040f054ebdbf33cceaadc94c55", "7120320c541c430fbd755710fb99e88c", "35e54871f85c49eabc07695b56dc07af", "cbc4715ba2324c0abcf368a98a4690f9", "cfecbec2dcb441759761a66b37fd494f", "e6f9aedc594649fc8d6f06dc236125fd", "bdcef93584444d8ebff5496d0e2052c9", "9b1fef1a37214f9a8749a6ebc86945ac", "ca6796380fd641d7b7b7c698c10164e5", "7413e97f04fb4100a866e97829a4010b", "2b721d7a534346ddae8d3edb145bce32", "c6906c539f784ab1b6d3e86d3a5422fb", "c66e21131e8249aa9dce2df561206958", "5df2b08d52774df189cf53582b3a7469", "c10cea2a089a40cd9ef0b2945bc4b464", "30332a5d596643f391132651500bb514", "f1292472717441da80c51a7eedf6decd", "e14eca9764d24ea4a46457186c8ec591", "9888ebda4afd49d68344916614658832", "6d6c5d95f16b4893985a69ec04e3e082", "f658d87fb8c14902b90461395b7cf3b9", "d25f71ce8e074102953eaa8b9dd2ded3", "b8c87cc737824bd2af14ecbaeca23a63", "5c0ab011fa8f4f0481d72da766d327b8", "21401f843ae844d89b74776fc149c6f1", "6567178200d9478ca80887d70c72e238", "22be11769aab43b8a59db2420fa36710", "425a65a49156434d9a5a9ef9b97077bf", "1ccdd29badb2495e8d6c09c707486723", "9c7c038bb28a4dd2b023246d1bd0af4c", "45b1c3272aed418986b8ef4b151e3a1e", "c502191d36224c379ddf0b07b7cc12da", "69e565877c754892951e1a3b12224ca6", "ec0162e35f394b9085b8662102f203f9", "42b903e6e5f042e1aa12846b6a0dffcd", "d98c012c201741b19650d891e9a9293c", "d985121b2d58482598f6acc1b079e99b", "bd7a09e0cde547c89bcbb45cc1eb950b", "8643697472204595bd3d706c0363ddf3", "06d15a741d45483abb139de39b244348", "0737c259aa9e408fbb7e55d9f78ae74b", "2e62772c4eff4eeab3436c29fe2eb301", "47dd29d044ba4a76b041fcc997ce8b50", "42ab438a3c0c4ded91d5d9aed0b3f46a", "7b402425eae744a18050c9633da09095", "f26696ab2b664caf8dad47168d034df5", "7de516b3d16747ae88336c4484c34d1f", "f291c644677f4a85bb276b549b92678d", "88a146bd2c7c4146b6b8987b2e7b6669", "4a745a7f4625423a9afc6ff30ccd6eff", "16315e6fe9af43d78f860ba95074bc3c", "eb2c33d060ef4f298143294d9a5d5cf5", "dbb5342bb2ba41c99e044e7f605241b6", "288febfce582450dbe9ade452de9ec76", "f2b063e363314c448a71f0672847f74b", "df22c12dba43423ea541331daa9dda51", "a6a3f74e998d4328ab267b57c87d7a5d", "9590afb19d6a42e9887f047f0a89992a", "b53a9587050540cdb33ce1fa10c8956e", "93788830d316436cb4029b8a60701d0f", "d382da753776442cb3b24b6b68f9bb23", "fa4500b0a47249d5b427c0a044cddae6", "1d7ac0b4466a44319a6a09a25ad92fc1", "88471aa376254c2e99465a72187aeae0", "a9387606b98f4a9e9bc45ef364f5eac6", "26c74d9da3094463b08d150f9406d7aa", "591b6d0316c94a6a815fef9adef06d83", "35adf891f8434cf8810e69025329ab80", "45a4f271d46746d1a272bdee3579f1ef", "f8acaf17e7f24f0da0eb609892c574f1", "233e4708fc0a4fda828a056add999090", "67066ae566894aeb9facb55e375fc3d4", "042f1d0d5df14b0fad412e3f7a1add8e", "da2575cd9e434c888439f44f3cc8fe58", "fd4d8462c5d744a8893f67cf36ee4add", "38b8b8ed1596447882adee4829ddb07b", "4b43b80a3ef54e9e9058d92c3a89ec12", "a0fb3bd8cf1746d3aa79c774811934ad", "9d2294c274ef49afa30d4c040e71f25f", "830758b021df49c6a96a4c427ae9a7d4", "fc44c63edfd04be090b6645cec72c548", "023abe4dc32047c5878b21cc973a1635", "a29cf1ea8b564b739912435e9fd66918", "d287c9ff10814a4d8f1153e1a1926792", "11966007c7ff4700a53d4161f2eaeaec", "e566b46c08aa4bd08e370f1324cadf73", "32c93d04cd8b4825adcd17f335f9383f", "a92be42d1cde488681ddaa810431112e", "e25e8c2611ec42c9b6321254249f71e5", "a0920f74eb4b440381eb3f10dcd9242c", "bbb05ac31e4b47288fcb7c8cb58cd07b", "e653c8301e9640bf924158a8c94dac88", "b016cfc8d59e40d09fffd350c2b9e44a", "2b42e8caa7164ef08bb53e89c6bb4d2c", "eb7864fbe1fa4c449d426c813611edca", "1a1548901acd41f09322aeb11d761aeb", "925e7607836442cab9e129ea6d7f310f", "7d42a962dc574e048868d66eb0ee988b", "8a6c160b813e47ed832d63b457aa5c82", "9cf30bb289bd4d13b34a5374bbf63e92", "2116956a10ed430fbe2c0ef62bc0e356", "b82942dbd861411884c0383628e1b05c", "2252434ec55c466982d3f03c467b8980", "4c9821928c034d4f94084668bfa0f413", "9ec2609414a24b1d8cb0ea878c54729e", "69aee64e87794d7398aadb120c47362d", "928db17f617f4be69318eb9de63742e2", "0c67b01f962a4f0788c2d4ba01a64410" ] }, "id": "-wHpHmD3zNSR", "outputId": "b3cd376d-c3e2-4e2f-874b-35caea9a78d6" }, "outputs": [], "source": [ "from langchain.vectorstores import FAISS\n", "from langchain.embeddings import HuggingFaceEmbeddings\n", "\n", "model_name = \"biodatlab/MIReAD-Neuro\"\n", "model_kwargs = {'device': 'cuda'}\n", "encode_kwargs = {'normalize_embeddings': False}\n", "faiss_embedder = HuggingFaceEmbeddings(\n", " model_name=model_name,\n", " model_kwargs=model_kwargs,\n", " encode_kwargs=encode_kwargs\n", ")\n", "\n", "def add_to_db(data,create_embed,bundle,name=''):\n", " batch_size = 128\n", " \"\"\"\n", " data : list of rows with an 'abstract' and an 'identifier' field\n", " index : pinecone Index object\n", " create_embed : function that creates the embedding given an abstract\n", " \"\"\"\n", " res = []\n", " vecdb = None\n", " for i in tqdm(range(0, len(data), batch_size)):\n", " # find end of batch\n", " i_end = min(i+batch_size, len(data))\n", " # create IDs batch\n", " ids = [name + '-' + str(x) for x in range(i, i_end)]\n", " # create metadata batch\n", " metadatas = [{\n", " 'journal':row.get('journal','None'),\n", " 'title':row['title'],\n", " 'abstract': row['abstract'],\n", " 'authors':row.get('authors','None'),\n", " 'link':row.get('link','None'),\n", " 'date':row.get('date','None'),\n", " 'submitter':row.get('submitter','None'),\n", " } for row in data[i:i_end]]\n", " # create embeddings\n", " em = [create_embed(row['abstract'],bundle).tolist()[0] for row in data[i:i_end]]\n", " texts = [row['abstract'] for row in data[i:i_end]]\n", " records = list(zip(texts, em))\n", " if vecdb:\n", " vecdb_batch = FAISS.from_embeddings(records,faiss_embedder,metadatas=metadatas,ids=ids)\n", " vecdb.merge_from(vecdb_batch)\n", " else:\n", " vecdb = FAISS.from_embeddings(records,faiss_embedder,metadatas=metadatas,ids=ids)\n", " return vecdb" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PfsK3DE4MMou" }, "outputs": [], "source": [ "nbdt_data = pd.read_json('data_final.json')\n", "aliases = pd.read_csv('id_list.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "JrGJh5XgNPvU", "outputId": "9755fd45-c17e-4c07-de8a-6896eeb6c875" }, "outputs": [], "source": [ "aliases.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423 }, "id": "CShYwGwWMZh5", "outputId": "e6525260-50af-40f6-90b9-3618fa12d88d" }, "outputs": [], "source": [ "nbdt_data.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SziJtbggMuyn" }, "outputs": [], "source": [ "records = []\n", "def load_nbdt(data,aliases):\n", " nbdt_records = []\n", " no_abst_count = 0\n", " no_journal_count = 0\n", " for row in aliases.itertuples():\n", " name = row[1]\n", " auth_ids = eval(row[2])\n", " auth_ids = [int(x) for x in auth_ids]\n", " papers = nbdt_data.loc[nbdt_data['authorId'].isin(auth_ids)]['papers']\n", " for paper_set in papers:\n", " for paper in paper_set:\n", " url = paper['url']\n", " title = paper['title']\n", " abst = paper['abstract']\n", " year = paper['year']\n", " journal = paper.get('journal')\n", " if journal:\n", " journal = journal.get('name')\n", " else:\n", " journal = 'None'\n", " no_journal_count += 1\n", " authors = [name]\n", " if not(abst):\n", " abst = ''\n", " no_abst_count += 1\n", " record = {'journal':journal,'title':title,'abstract':abst,'link':url,'date':year,'authors':authors,'submitter':'None'}\n", " nbdt_records.append(record)\n", " return nbdt_records, (no_abst_count,no_journal_count)\n", "nbdt_recs, no_counts = load_nbdt(nbdt_data,aliases)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "e360e5e9c664412ab02f0d4738010d1a", "c4f1f5bf33da4ffb91928e18f499c5cd", "4f9b213a844048a39f5666c2b67903c6", "9fd71519918646b4ae7438a37b6cc738", "aeffc2c00fd34ebc936caf831241ea0f", "62d9dff282fd4eca90d261c36bf9d4b5", "36032cad6a27480abaadc56b10b845fd", "3d3f0e54ccef44e0b7e1586dc0870800", "130d77d72d02433398695867db2ae56c", "110be3028fbb401a9fe69df2c86761f7", "b34343c171034370979ab27bff9bef2d" ] }, "id": "IovTlDINc2Ds", "outputId": "753f537f-1cae-443f-c120-1f42f1529946" }, "outputs": [], "source": [ "nbdt_db = add_to_db(nbdt_recs,create_miread_embed,miread_bundle,'nbdt')\n", "nbdt_db.save_local(\"nbdt_index\")" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", 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