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"StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/gist/GTimothee/cb3551ba6eb14b04f7a06d63ea4616f9/load-and-save-embed-model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "code", "source": [ "!pip install datasets\n", "!pip install --upgrade sentence-transformers" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "pa9wkGU7i21H", "outputId": "d2b055b2-70a2-4b79-b57a-7e0e2aad8f1e" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting datasets\n", " Downloading datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n", "Requirement already 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This behaviour is the source of the following dependency conflicts.\n", "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n", "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.2.1)\n", "Collecting sentence-transformers\n", " Downloading sentence_transformers-3.3.0-py3-none-any.whl.metadata (10 kB)\n", "Requirement already satisfied: transformers<5.0.0,>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.46.2)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.6)\n", "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.5.0+cu121)\n", "Requirement already satisfied: scikit-learn in 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scikit-learn->sentence-transformers) (3.5.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (3.0.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.4.0)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (3.10)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2.2.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers) (2024.8.30)\n", "Downloading sentence_transformers-3.3.0-py3-none-any.whl (268 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.7/268.7 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: sentence-transformers\n", " Attempting uninstall: sentence-transformers\n", " Found existing installation: sentence-transformers 3.2.1\n", " Uninstalling sentence-transformers-3.2.1:\n", " Successfully uninstalled sentence-transformers-3.2.1\n", "Successfully installed sentence-transformers-3.3.0\n" ] } ] }, { "cell_type": "markdown", "source": [ "# case where we want to add tokens" ], "metadata": { "id": "vEd-C3T8mwWJ" } }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "9liyq3bLICqc" }, "outputs": [], "source": [ "# from sentence_transformers import SentenceTransformer\n", "# from peft import LoraConfig, TaskType\n", "\n", "# test_new_tokens = [\"[bla]\", \"[blub]\", \"[bloo]\", \"[blib]\"]\n", "\n", "# # Create a resized base model\n", "# model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n", "# model.tokenizer.add_tokens(test_new_tokens)\n", "# model[0].auto_model.resize_token_embeddings(len(model.tokenizer))\n", "# model.save_pretrained(\"all-MiniLM-L6-v2-resized\")" ] }, { "cell_type": "markdown", "source": [ "# Training" ], "metadata": { "id": "7uZFsDi5if_z" } }, { "cell_type": "markdown", "source": [ "code mostly copied from https://www.philschmid.de/fine-tune-embedding-model-for-rag" ], "metadata": { "id": "gZ_uW1DVnbZW" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "# Load dataset from the hub\n", "dataset = load_dataset(\"philschmid/finanical-rag-embedding-dataset\", split=\"train\")\n", "\n", "# rename columns\n", "dataset = dataset.rename_column(\"question\", \"anchor\")\n", "dataset = dataset.rename_column(\"context\", \"positive\")\n", "\n", "# Add an id column to the dataset\n", "dataset = dataset.add_column(\"id\", range(len(dataset)))\n", "\n", "# split dataset into a 10% test set\n", "dataset = dataset.train_test_split(test_size=0.1)\n", "\n", "# save datasets to disk\n", "dataset[\"train\"].to_json(\"train_dataset.json\", orient=\"records\")\n", "dataset[\"test\"].to_json(\"test_dataset.json\", orient=\"records\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 215, "referenced_widgets": [ "9cbddb365876477396610bafe3114c00", "19410f97eaaa439c9490a0723a73afd2", "95aef1be81954aa695fdf22acd4e4d87", "b1a91a365f3a45f7b44b79ccb2ce77ba", "fd8ecc5a054942149654358cf9e2ec17", "5fd450cbada54c62bbefc6f4734a89c5", "efe02cf429ad4d44ae818b0dac9d92ca", "b5e7f9ca0cfe4415a7f01dd209374146", "041d5fe152ef4cab8ead5a9fc2726c61", "29f27034291447c48314acdb49bf873d", "09152f0b1434472682302a8f614ac43d", "916b53848a2e45198d1ea3afdcd4e3c5", "b40082d83d7146d7b61b0372b01d8101", "a2055efc2050458ab84ebf61bc95ac98", "cee1c4bbebce4f2a8e7f75edabdcd9ce", "ae67ccd94a544b3f95e5f2c817e51b6c", "1c50180af28d4bed84162aa4579a84d3", "b736bddf47624d0caf41c6c10c0a7cc7", "68e0d6fbcdbd449b8b929d870cb7c1a1", "88b0f1189e484111be5429d4edf8e75c", "12439f806a57479bba74bcc3a4c3866d", "8cdcc4a75d4c4881829f7f0bd0762e98", "62a80de00a2443e2a325f4ec0bb2ac9e", "b6ce7b55d3d34218b950a429fd5c9126", "85a6e4ec1a144e7481a84a947a47380f", "0e89752fd2d145e7a9fa4c6196c4057b", "38ea66bfbc8d43a4a8842016f14e6d80", "fe1c01dbfc364289863f74355d18ef45", "7494f938b1bb40cf894fc0fac317005b", "b1197cca631b42a6adbf1e9ada917a90", "7c5d2a2d0d9a4b8d86ecec51bf809850", "6110829a7f3a4424bd673182ef8b1986", "ad216e7cdbde45f896cb2711cc5f5536", "f64e849e8ce742c0a68dfe942f1e8f7a", "0bf3d6f38b6648f7b8a32baf63a2614e", "be5692f3014c48ed83bb822a21f22626", "ba0a764c7b9841ad87bbe1a367539423", "e46320f6df5644ab9ef1ded1537f80db", "5bca1e03a8324d36918f85493c8ac313", "9532b0e03a414582b06849b01983da04", "206af04e368c43f595d575c8cf5e17a7", "152cdc33f25641fe86ca15cd68e3bfb2", "b14b9845e4454d6b849dbccc939156b2", "1a5346f7c92941e4ad3ecd96b774bb42", "64a4c4af79cf42859d519fffaa86be8a", "bf0b39bbad4344cab1b301eed03c6b57", "3d211fa50f7e432aa0fe2fb9482c157d", "313ac07480a944919614b9223990385c", "6a28925540d34c0385243de273f5a6e1", "07f6d1372c8d43fda9c57adf69933eaf", "fd703f4046814e89ad550bf00a370dfc", "c3035f020f6e430e840fb5ce998f2032", "e683fe0b747849caacc8cf93106c1ea1", "dd584dcdba8544fdabe5ebd0ac3130a6", "3faa44446e684f3fbaec3622875c93aa" ] }, "id": "KhCNyIWpiKzq", "outputId": "47cc5d20-eaaa-45e6-c445-10d3a5063424" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/882 [00:00<?, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "9cbddb365876477396610bafe3114c00" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "train-00000-of-00001.parquet: 0%| | 0.00/1.09M [00:00<?, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "916b53848a2e45198d1ea3afdcd4e3c5" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0%| | 0/7000 [00:00<?, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "62a80de00a2443e2a325f4ec0bb2ac9e" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Creating json from Arrow format: 0%| | 0/7 [00:00<?, ?ba/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "f64e849e8ce742c0a68dfe942f1e8f7a" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Creating json from Arrow format: 0%| | 0/1 [00:00<?, ?ba/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "64a4c4af79cf42859d519fffaa86be8a" } }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "242472" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset, concatenate_datasets\n", "train_dataset = load_dataset(\"json\", data_files=\"train_dataset.json\")['train'].select(range(500))\n", "val_dataset = load_dataset(\"json\", data_files=\"test_dataset.json\")['train']\n", "corpus_dataset = concatenate_datasets([train_dataset, val_dataset])\n", "\n", "corpus = dict(\n", " zip(corpus_dataset[\"id\"], corpus_dataset[\"positive\"])\n", ") # Our corpus (cid => document)\n", "queries = dict(\n", " zip(val_dataset[\"id\"], val_dataset[\"anchor\"])\n", ") # Our queries (qid => question)\n", "relevant_docs = {} # Query ID to relevant documents (qid => set([relevant_cids])\n", "for q_id in queries:\n", " relevant_docs[q_id] = [q_id]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 101, "referenced_widgets": [ "80e92517c9f145a39a99e33e1e16e2d1", "c4ba4c497256469aa2dec03033ddb4a9", "252e19c2da1144cb94102e62d3459f59", "2d90e43a971f4eacab56ce3e494dfcde", "de7e82f565a3499681b70e019c73419b", "a1a0106be92c4e3299ccf69ed3f5ed53", "d681e917c28246c0a21c2b119a3b7cdf", "d934caa6fc994347b98a867277958890", "d0a328479e60412792d0b3037c52af0d", "6a6d360a82f747c1890856ccb9638dc1", "7a28435bad274516845168f44e788aed", "70b5083ce95a4a04b9f2f3d308af4ad1", "4a269035e9cc4daa8898c385dc31dc14", "f5dd089f2a734b4b982bee5504fd4b3a", "1aa73d826ca64292991cce3fb4c95e70", "e9a4dbe65d07464790140489bf5b5aab", "6bad770bdbbd4c44a3e1e1da2fbcf62d", "fc04c3cabc9e488babf3f4fbdf8bf6b5", "153841d471e34380a9039e997403965e", "200a90588fa34458b06f6622b0d85618", "3f202635b51c4fab8ac3ab14d57fe4cf", "5bd8e1e3b6084ccf8953f207c046c57b" ] }, "id": "IocKs1k-iqXe", "outputId": "352b3cd7-0566-4bdc-90d6-876fb63350f1" }, "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "80e92517c9f145a39a99e33e1e16e2d1" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0 examples [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "70b5083ce95a4a04b9f2f3d308af4ad1" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from sentence_transformers.evaluation import (\n", " InformationRetrievalEvaluator\n", ")\n", "from sentence_transformers.util import cos_sim\n", "ir_evaluator = InformationRetrievalEvaluator(\n", " queries=queries,\n", " corpus=corpus,\n", " relevant_docs=relevant_docs,\n", " score_functions={\"cosine\": cos_sim},\n", " batch_size=32,\n", " corpus_chunk_size=100,\n", " show_progress_bar=True\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 87, "referenced_widgets": [ "055a26e0b776423da6981e229d86c921", "2441da0d7f6145faa9974dfc5b5fc2fa", "eec3a3534c454c40ab25c2bfc84b8d3a", "5d3e742d1cf4477aaafab18b8c2acdaf", "7e2c3479c93d4791964a32ba04cbcc71", "4ce717e9a3d444a0b552e50a073a4650", "5cd70932ae4a4486ad741253e3f93f49", "ebca68ef74c44b81a7ce13a2e232657b", "62b68c36d40c40f18eb0066e13e69946", "bc62323b9b764fb2a553d5a63794e719", "7836a1b191184c55b2f9ca54e37f4a69" ] }, "id": "GAluOJ46i_ck", "outputId": "edcfaec7-9a3a-4c4f-cffe-97f58616d24e" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "0it [00:00, ?it/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "055a26e0b776423da6981e229d86c921" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from sentence_transformers import SentenceTransformer\n", "from peft import LoraConfig, TaskType\n", "import torch\n", "\n", "# Load the resized model and add an adapter\n", "# model = SentenceTransformer(\"all-MiniLM-L6-v2-resized\")\n", "model = SentenceTransformer(\"all-MiniLM-L6-v2\", device=\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "peft_config = LoraConfig(\n", " task_type=TaskType.FEATURE_EXTRACTION,\n", " inference_mode=False,\n", " r=8,\n", " lora_alpha=32,\n", " lora_dropout=0.1,\n", ")\n", "model.add_adapter(peft_config)\n", "\n", "embedding = model.encode(\"[bla] my name is [blub]\")\n", "print(embedding[:10])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 425, "referenced_widgets": [ "512c3f6e7f46498ab5baeb3bc600286b", "e3b531109a31443a9bef63cdbd087979", "d69a43182f7349c0b62ff78e6773954b", "39f6aa29587e4d94b6bc9bbdc950b060", "4d9d4697cb41442782c75bfb9e292b7e", "f5c0dfc8272946c4ad7dcad549e1b2be", "ce22869496de451ba2c6e9744a0b9252", "361110a0921f4a03b2311e0ce0ccbd11", "fd120dba4023440aaa1abe0561f57c8c", "944f16583de14e60a062047fef983968", "45ae58782dc84c65b9fa62a6f700ab8f", "ceecd89da7ff488a9e1b35c003757423", "7d6b130d6377493284864d308e3c2a3a", "706a4ad9f069418d9f97244044f6fa68", "38f746f3f7554beaab8e661243433fa1", "dd609325f92e4cbfb74cc14887e2b59f", "0cb4a9f9e33d4ad8b92cd1a3d587e52d", "b98c25e51c5047b78399ad8cfe803be3", "0d6203319af649faa1cf82154d10663b", "6a2066123629464aa16f5e3c26001805", "e6261165a15a4f09b34a3e77ef653a8c", "2c4f2f7431124e71a1c4f0de95d7e884", "ead38be0c1764ecda6314de8f28cf00c", "7f15bba8b7664423b2c52b01e291e2a7", "6cad7a1876a24ae6877917323117c09c", "2679e8f8764e4cc2b67e91c614d54119", "b6eb21064fd04531bf968b6e1534d076", "10577730987347f2ba8968dc6002c958", "da834da28fa54b06b454d45c2b545e05", "bd2cb42e05fb42de9494c76689a43eaa", "8ca3aa28b5574403987fcaa17d6af06b", "f8ff32932b6841ed9deaa1222ab810e2", "1200dd13880a4b4fa547240608cd6c57", "7d0e4b7b356b4a4ab1cbffeb6ff5deaf", "2d92117bdc7248fa841f61fe4a2f81d3", "53c69d8084db429598152acbca843aac", "53392ecc2beb4226a14866c16e38c16f", "d71d644d81a24396b23b6212517d90ee", "f7f1f08e8fd14f24b79af5b7496a0a55", "839888403c6442cf8c760d4bd8fb6833", "120f3cf6a6ba434a898f8d313595e677", "f448e7bd88a24d8697e17e38e7617a9d", "5710ec1bfaf44be5989ac5a4f23febf8", "a342066d864e4957afe942363b09edd0", "a51ba8f06d954acbb3d6998d6e0e408b", "f55d84fd88ce42429b45fc4472f5c5a8", "1ac1c2de38b741b3ad0f86a4d1a3e6b0", "86a1bb352c1741f88cbc78c275728359", "11deccb69db84f90a23d444f9d3f55c8", "5f99d6ae0c3b4be89f754f2f9138f40b", "696afdccf9054c13abce1cda77bcb451", "28b86cd011c84fad8148922d5a769cdd", "65c7f208acf8404f858b58f20ebda3d1", "11a3155dac544c4f8357bb36390476f0", "600e222c41b74301b97724e1f5733e17", "76391703e5934d3aa7f607ad0cb0fc17", "30ae774d3f7344f486832d30dae475e0", "00420d11ef37439c958e5529ea6add49", "364f446e550243b0b7d16dd6a03a01f9", "b4385b0caefd401d960cfa0efa072efe", "61a70979d8ac47e388308a4c89a96a2e", "10f92af1c4c44eab9e3573ab97fbde55", "5f6fd1c2b93345c090e6aee4f61d9c32", "8ed890fe4dcb4a00a7c9f74449c85470", "a4adae533ca8425aa876aec82005b88b", "65bb127f70664e73badb6bc7be00849f", "02e828d6ee054174a378d26b82b879fa", "550da247faff4aa88051238fc094417f", "79d2febfdeb444e88f05452da2e5ab94", "548d453d1ef349a3bdd694395cbad553", "b7eee786cfb04cbebb4bf13834c705dc", "a4e7783eaf0e4b4eac25d5f770f6edb4", "ec6099b75719425fae2a849c75cd8efc", "97d8c1a1a0624521b11b79aa0d62a5bf", "b289b3a6a1704cebb0e877cd792758a4", "8ca08a9ab7e54865a1f0b55124b0ab2b", "b4b4138101474bb7a3531728e49f9c84", "f13edb6d94ee481ca2e96e9a9c7186ea", "5d041cccf2a94a58a114b90394d04e83", "03c7063d54fa416baaa4b8c2c588525c", "7642612a5b7c4fa290482d8549d7e71a", "4cd10c873d6f4b3095902201ec365577", "7bdcfb21ccc24895bc3fbef16cf8ebd1", "9a16705b033641b1b912282f464d673e", "aac58f4b83a3411cb16187faaf74e28b", "88603c0774104dd7b9e526ecc9806aeb", "5fd40efceb0e44639114dc9b8971e45a", "02163733bf24498e90875184c0234d52", "29c0c51b280849e2aa9955cdc7144b22", "c87ba18dedfc4b7f828987c07895fc54", "d7b01f1007ac4b47a476fdcc213f2b64", "443e7748d69142a0b907bcc25a40c182", "3786ea62a32d4b5da0d079941d3947da", "41a7292a8475494aac93a1511f5b5eaa", "d5ddea4ae38643d99ca189a758b5a2b1", "aa418bf2475b4cddb64638e9be4f15b4", "7d03cc12e98241f898d893144fca8c7b", "656c8b0f9d7f4ad784f1a77cf843d2ea", "39b62c0fd52342d4aface703aea4058f", "27732f2ca3c440458e6099a035a1018f", "6daa507d770a49908ecf2ee07f7dddac", "c50bbd7f7be74b4f85e13c35c238709b", "d514cdcced20484a8d4e2ae85736e77e", "9b426c4ba1c041ad864b6d570773e3eb", "8a617ad19a8b442892a82ef0d34c8361", "afcf40e8a68d4edebed2b9629524e89b", "36ca454fb3cf497593aded6589d1b934", "16c33edfdd644f7bbcad97f89010ae79", "73fda7dcdb20472ba1b7e3fed9faa451", "7bfd0d6700f9431fa436921d26165678", "e31cc7cb44bf457ebef36c0b2d74b92f", "8cac013c3cd24801855082e52d0edd06", "8e8ac08be2a54f6f9fcadbe18b257537", "e850a65f7e09477bb15757dbd7e8042b", "a23a944a566848a1b688063eeb7f355e", "73abbee8c18143098239916cbb043db8", "0122b1b060e943b89ca76876688e0be8", "09cb7fc9a46640369cace294b7bbaa28", "eec8702cd2064142ad70de8a18ee4ada", "34e19fa5daea486888df371f6861f04d", "553d28d720a44487bb95d2bf5829dee8" ] }, "id": "qQq-7SDHIhIA", "outputId": "2a5ba1d2-6400-409c-d54b-b89767f3a23d" }, "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "512c3f6e7f46498ab5baeb3bc600286b" } }, "metadata": {} }, { 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"metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357, "referenced_widgets": [ "01df1dcefc054dedae762cb7f181ddbd", "01774c8db33b42ba9198199c9b89e633", "626320c80eb84c77bffd20385a2d53e2", "21b7126ac4c643e4a971550b432d8e5b", "23a4656e337e41b88da26c23c033e05c", "b5cc6abce44a476196ac72a82d8e24da", "0bc2ace14bb94596b93f374b85c53eab", "a58717641b90407492a872bb3e616600", "50953885582d495782924e5278512857", "445f031729144ee6ac6279ee8e48ab45", "5c7c2a39c5584fdcbf6dc6de323f0421" ] }, "id": "IF63o9kbljdb", "outputId": "9964243e-4e11-4d34-9ed5-1012d72d0bbe" }, "execution_count": 8, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Batches: 0%| | 0/22 [00:00<?, ?it/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "01df1dcefc054dedae762cb7f181ddbd" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Corpus Chunks: 100%|██████████| 12/12 [00:01<00:00, 7.58it/s]\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "{'cosine_accuracy@1': 0.7842857142857143,\n", " 'cosine_accuracy@3': 0.8914285714285715,\n", " 'cosine_accuracy@5': 0.9171428571428571,\n", " 'cosine_accuracy@10': 0.9457142857142857,\n", " 'cosine_precision@1': 0.7842857142857143,\n", " 'cosine_precision@3': 0.29714285714285715,\n", " 'cosine_precision@5': 0.18342857142857139,\n", " 'cosine_precision@10': 0.09457142857142856,\n", " 'cosine_recall@1': 0.7842857142857143,\n", " 'cosine_recall@3': 0.8914285714285715,\n", " 'cosine_recall@5': 0.9171428571428571,\n", " 'cosine_recall@10': 0.9457142857142857,\n", " 'cosine_ndcg@10': 0.8678902907115743,\n", " 'cosine_mrr@10': 0.842611111111111,\n", " 'cosine_map@100': 0.8451141243804458}" ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "code", "source": [ "from sentence_transformers import SentenceTransformerTrainingArguments\n", "from sentence_transformers.training_args import BatchSamplers\n", "\n", "args = SentenceTransformerTrainingArguments(\n", " output_dir=\"training_trial\",\n", " num_train_epochs=1,\n", " per_device_train_batch_size=2,\n", " gradient_accumulation_steps=1,\n", " per_device_eval_batch_size=.5,\n", " warmup_ratio=0.1,\n", "\n", " learning_rate=1e-5,\n", " weight_decay=0.001,\n", " fp16=True,\n", " lr_scheduler_type='reduce_lr_on_plateau',\n", " report_to=None,\n", "\n", " batch_sampler=BatchSamplers.NO_DUPLICATES,\n", " eval_strategy=\"epoch\",\n", " save_strategy=\"epoch\",\n", " logging_steps=.2,\n", " save_total_limit=3,\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"eval_cosine_ndcg@10\",\n", ")" ], "metadata": { "id": "y_cs61pEjaZl" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "from sentence_transformers import SentenceTransformerTrainer\n", "from sentence_transformers.losses import MultipleNegativesRankingLoss\n", "\n", "trainer = SentenceTransformerTrainer(\n", " model=model,\n", " args=args,\n", " train_dataset=train_dataset.select_columns(\n", " [\"anchor\", \"positive\"]\n", " ),\n", " loss=MultipleNegativesRankingLoss(model),\n", " evaluator=ir_evaluator\n", ")" ], "metadata": { "id": "c8Tz9Ea5jjqC" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "import os\n", "os.environ[\"WANDB_MODE\"] = \"disabled\"\n", "trainer.train()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 598, "referenced_widgets": [ "1e3cf7b8d8cb43d1a6e6c4ed3e230c60", "9349e5af702d4ec190e0c5b3a72b680a", "cf17822f874e4345bf6e545e97a5a3bc", "89cf6f27e2d3456a90f82b4f19957270", "c6b877db432f44379d98b5deabbe0ac9", "f4884675f6f84016af0fadc9817a251c", "81d94a9dece2450c9de2a4d64f7be9f1", "b43abb72c0654d6a862410bab00672ab", "e0a2a1c4c10e4887bd6a27ae466f3651", "409af25ed298495aba779d3989b0d52b", "9b515ddee3b24540998dc6e2080ec5f1", "842604dd1db9498fa7e826f375514479", "5aa2596669754c8d81aa6bfedab007a9", "1741b8aca8d34f2eafef7e0452efaa3c", "b511fcad5c5b496283d7e1e05f8c6db7", "b38f8c334d5d4d869d42df75e0a36499", "c3e59487742446dca9c607090ef46f77", "a365cc7737a84504839836a6b24da465", "e676e64e9af445f7b9fbcd59a0c007e9", "5c5161841c0c42a295f0ceefaebffa09", "ddb20b36016e483f9549fdc1ebdebf26", "246566b5287842a29dfde452936afc38" ] }, "id": "FRBTKuNXjlND", "outputId": "1673e970-4bf1-4ff9-af82-87b2dbb06694" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "<IPython.core.display.HTML object>" ], "text/html": [ "\n", " <div>\n", " \n", " <progress value='251' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", " [250/250 00:12, Epoch 1/1]\n", " </div>\n", " <table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: left;\">\n", " <th>Epoch</th>\n", " <th>Training Loss</th>\n", " <th>Validation Loss</th>\n", " <th>Cosine Accuracy@1</th>\n", " <th>Cosine Accuracy@3</th>\n", " <th>Cosine Accuracy@5</th>\n", " <th>Cosine Accuracy@10</th>\n", " <th>Cosine Precision@1</th>\n", " <th>Cosine Precision@3</th>\n", " <th>Cosine Precision@5</th>\n", " <th>Cosine Precision@10</th>\n", " <th>Cosine Recall@1</th>\n", " <th>Cosine Recall@3</th>\n", " <th>Cosine Recall@5</th>\n", " <th>Cosine Recall@10</th>\n", " <th>Cosine Ndcg@10</th>\n", " <th>Cosine Mrr@10</th>\n", " <th>Cosine Map@100</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>1</td>\n", " <td>0.012000</td>\n", " <td>No log</td>\n", " <td>0.781429</td>\n", " <td>0.894286</td>\n", " <td>0.917143</td>\n", " <td>0.945714</td>\n", " <td>0.781429</td>\n", " <td>0.298095</td>\n", " <td>0.183429</td>\n", " <td>0.094571</td>\n", " <td>0.781429</td>\n", " <td>0.894286</td>\n", " <td>0.917143</td>\n", " <td>0.945714</td>\n", " <td>0.867046</td>\n", " <td>0.841435</td>\n", " <td>0.844011</td>\n", " </tr>\n", " </tbody>\n", "</table><p>" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:418: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Computing widget examples: 0%| | 0/1 [00:00<?, ?example/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "1e3cf7b8d8cb43d1a6e6c4ed3e230c60" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Batches: 0%| | 0/22 [00:00<?, ?it/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "842604dd1db9498fa7e826f375514479" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "Corpus Chunks: 100%|██████████| 12/12 [00:01<00:00, 8.50it/s]\n", "/usr/local/lib/python3.10/dist-packages/transformers/integrations/peft.py:418: FutureWarning: The `active_adapter` method is deprecated and will be removed in a future version.\n", " warnings.warn(\n" ] }, { "output_type": "error", "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: 'training_trial/checkpoint-250/pytorch_model.bin'", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-11-af73f3eb6fa7>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"WANDB_MODE\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"disabled\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in 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