{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "tf-U0kGCrI0N" }, "source": [ "## Jina Embeddings Top-Performing Open-Source Bilingual Models Now On Hugging Face\n", "\n", "In this tutorial, we'll download the Jina Embeddings v2 bilingual German-English model and use it for cross-langauge information retrieval.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "v7SrKjNWrevC" }, "source": [ "First, install the necessary libraries: `transformers`, `faiss-cpu` ([FAISS](https://faiss.ai/)), and `bs4` ([Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/)):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4p2ZX5mZrFqm", "outputId": "7bbdb975-8d3c-44dd-e0d8-7cf233323f84" }, "outputs": [], "source": [ "!pip install transformers faiss-cpu bs4" ] }, { "cell_type": "markdown", "metadata": { "id": "1LLxxRSNsJeI" }, "source": [ "Next, you will need a Hugging Face access token. Sign up for a Hugging Face account if you don't already have one and [follow these instructions](https://huggingface.co/docs/hub/security-tokens) to make a token. Then, insert your token into the code below and run it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PWfrrhARr1U1" }, "outputs": [], "source": [ "import os\n", "\n", "os.environ['HF_TOKEN'] = \"\"" ] }, { "cell_type": "markdown", "metadata": { "id": "MeRH8A6ltUcD" }, "source": [ "## Download Jina Embeddings v2 for German and English\n", "\n", "Once your token is set, you can download the Jina Embeddings German-English bilingual model using the `transformers` library:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 513, "referenced_widgets": [ "2aad4faedb1c4eabbf741894a566870e", "be61df8b3f9542c6bd148109238d16c5", "38c17d13167c4146893e4cbefa0b0ba3", "e6b7ed8f50d84923846a861b6ad2c952", "eae2aaf11dd7474ca76cf6e36a4502e6", "3890c971ae354779a56497c1eccf96d5", "a2a057a5360d445da8b643a990c186af", "d1d612b6ec364186b1388cf2381712ae", "71655f3d18fa472495b99a6948f7ed7a", "3be812069f004614a563cdae740af785", "48793929ac764c439a9e6bc01a57bd67", "b2c721df97ad46a6babc17c73744dcf0", "13561ea3849843a3a560ae3939799f1b", "c7bb5c217013408b86b4c73b816ca5e7", "3485fe831dc048e5bec924d650b04a09", "db9ba257bc9044c7b84da2b02a7ac27e", "0ab006f2780048ed949b385c3e5b3d5c", "b3a3fb5d4a1f46549cf03d1fd601e2c5", "7ee65b7881c04c4f9517da7fc6c9f133", "bde024f6fb1a44d7af8b9ec05c7c13d2", "b2a5da9557c24330ae77c0418af67fb6", "9e19ec6b4dcb493cac60b50464ae9147", "b3f57747f5ed497782ba7d50db47bc08", "306deddd809942f699e42d6c73f72476", "59e238b472624af2b3f55d6d1f33b880", "b8cc1414ee9446438345d96a1a94cdd6", "cf783bad7b3a48dcae9dddcdf95adbed", "ff19abdb70de49a4814a2b57f61b7867", "c2a44a55a32c49d8bb32a0bf183b444f", "ba580174e1ed45caafa2d54093907efb", "4a4f807a7d28463ca3d08109e8e26f5a", "16d6a5924b5d4a64b76e9b6e0d0d2a5f", "34b57a4c5f114a828f5a63de762f1218", "779db910f0f04f8e8bb1dd7ffc29d067", "250b54ec8f814d71a7a9a0aea1af9cae", "d972c22555ac40b18a2d888fe86f92ac", "56ab53e268314b8e83690104bceea7a5", "cc4763e11cd346c2b447a3b27d4c49af", "369c2bdfefd0479db21deb0c0b46371c", "336c86f4180d41eabe78ca5d10b88ec0", "7ecaa964134c41ba98072c8b98b4bbb8", "9b05bacea09449fd9c3387257d43989a", "9be73f1c4353458897d0eba2e1be2394", "522cc4ad2d4e43f68c832e4b0953f313", "6bf01017e1254a2c952b86c6fa75e6ba", "51ae888b33244a21b0907a95c3bbd908", "067d029469ac45b9ae4566e8935666e9", "f0efd4bb43374dddb8f385c4f4d48f29", "5021028a26314d33b7c8ca6644fca42b", "c82b541b150242c1a18f06c6abb75c16", "4bb73b2f0a7d4c82a1976496803aa6fa", "2140849d80e64ab0bfc32adfac6e32a5", "66b3cf3a6ef04c428df897402b7eaac4", "bdae77e73def465f94cf79e9c46b6b05", "34e3982ecb0a4cc48c1864284c1565e7", "a0fe47ec3d4944abae38a86c1aea6582", "88b35b95a2b2489bb44309193a8f98c8", "03c7dcc0001c4c3383fedff3e7321836", "71a389ebc3184d209da58f3d3a2d49e9", "af76f1005c504206a7912d7741ec9a8c", "45abd30768b047b79252c5666aa25bf3", "2331ea5b3a914e5285630714919ae2fb", "9e86df6395eb43d58b78a31d33d6d932", "d38b6cc3dc5d4562b4c6876a3c26683f", "d7a16e04bf224c6f9a45993a065f6eeb", "9dced911479a49a9bcf31318d980674a", "f3b0d2f45d26458c904cdd97d2bce5fc", "1d7eee86f92f49b1803163078026ac06", "51548974b45e4fa59961f872a99f0c9e", "7eaf6d37da9545a6a2a2eb80fa41affe", "7a983fdcaeff4c0b99f2ad36f2227c12", "b2d2bad25e0e43db8f812e30101d291d", "e51116770c444ab8a7985fd178287254", "11677c86b1de4ff69946657bac9c2077", "af22b1171fef4258af25e972cbb484bd", "ced86edf22a442a29ff5fec0451ba942", "97b4033963dd4fb58a878fbe3cda6fcd", "64328e488e7c4b88a57856b59d5f80a6", "cea7dd9e4c224f09b44fbcb9b1d7c4e8", "c9d1eac158ca4a50b71fee234033684e", "1fa92a79d2c943318fb4a38afbafef74", "e8474cef90394d14af91d8d2573462b0", "c757d009377341e4aaee0254173b9248", "5029e54bf43a4e37b66e8ae04cf28026", "0303a58baf764a3488157c35083c7bee", "b8e5d550e6e748d783cc718e1d96d6e3", "b82e9a46b9404ef1bfc4f3645dc1675e", "6e45b291f5cd42bfa5040ea66e22d5bd", "9c1d17d983224af99f82fbcf7511a3ff", "856c362a1a434b75be4e0738e0b6afe9", "27b440d5fdb84267aab12ceb25033826", "6cb2c0e32bcd46879d1ca30360604254", "c58a6696422a4e4d945e7a962a4e2acb", "a455e8d42aac4dc99b700f17b4b7ef2a", "6c101da2004f4b18854a598691fc4d4b", "015ecfa1e9404062ac8bb501087db235", "cf9cc65117744c5fa416b18451780419", "ae791c36cbe84f7a987387a8b9f6f836", "7975e9e5f19e463da203d5024c78df51" ] }, "id": "6Ljd3m2VtMgF", "outputId": "d466018f-4f2b-4475-c297-f62ad6708ca0" }, "outputs": [], "source": [ "from transformers import AutoModel\n", "\n", "model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "XySKDxPptw0I" }, "source": [ "## Download English-language Data\n", "\n", "For this tutorial, we are going to get the English-language version of the book [*Pro Git: Everything You Need to Know About Git*](https://open.umn.edu/opentextbooks/textbooks/pro-git-everything-you-need-to-know-about-git). This book is also available in Chinese and German, which we’ll use later in this tutorial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FZaQMnVitYk8", "outputId": "2e4234e3-302e-4d03-b3cd-421ca472675a" }, "outputs": [], "source": [ "!wget -O progit-en.epub https://open.umn.edu/opentextbooks/formats/3437" ] }, { "cell_type": "markdown", "metadata": { "id": "kK78OURFuIFo" }, "source": [ "## Processing the Data\n", "\n", "The function below opens an EPUB file, splits the contents on the `
` tag, and stored it in a Python dictionary." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TJpbImqwuARZ" }, "outputs": [], "source": [ "from zipfile import ZipFile\n", "from bs4 import BeautifulSoup\n", "import copy\n", "\n", "def decompose_epub(file_name):\n", "\n", " def to_top_text(section):\n", " selected = copy.copy(section)\n", " while next_section := selected.find(\"section\"):\n", " next_section.decompose()\n", " return selected.get_text().strip()\n", "\n", " ret = {}\n", " with ZipFile(file_name, 'r') as zip:\n", " for name in zip.namelist():\n", " if name.endswith(\".xhtml\"):\n", " data = zip.read(name)\n", " doc = BeautifulSoup(data.decode('utf-8'), 'html.parser')\n", " ret[name + \":top\"] = to_top_text(doc)\n", " for num, sect in enumerate(doc.find_all(\"section\")):\n", " ret[name + f\"::{num}\"] = to_top_text(sect)\n", " return ret" ] }, { "cell_type": "markdown", "metadata": { "id": "bbY1RlCvu6L1" }, "source": [ "Process the book you just downloaded:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "bYaJknu0uabw" }, "outputs": [], "source": [ "book_data = decompose_epub(\"progit-en.epub\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ZDRJLDWwvGH0" }, "source": [ "The code below generates the embeddings and stores them in a FAISS index. Set the variable `batch_size` as appropriate to your resources. Colab without extra memory appears to work well with it set to 5.\n", "\n", "**This may take some time, depending on the speed and resources of the system you run it on.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0MKxviDEu9nl", "outputId": "44c92ecd-d418-43b3-e13b-bf97e0361d54" }, "outputs": [], "source": [ "import faiss\n", "\n", "batch_size = 5\n", "\n", "vector_data = []\n", "faiss_index = faiss.IndexFlatIP(768)\n", "\n", "data = [(key, txt) for key, txt in book_data.items()]\n", "batches = [data[i:i + batch_size] for i in range(0, len(data), batch_size)]\n", "\n", "for ind, batch in enumerate(batches):\n", " print(f\"Processing batch {ind + 1} of {len(batches)}\")\n", " batch_embeddings = model.encode([x[1] for x in batch], normalize_embeddings=True)\n", " vector_data.extend(batch)\n", " faiss_index.add(batch_embeddings)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Verify that we have 583 embeddings stored in the index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# This should be 583\n", "faiss_index.ntotal" ] }, { "cell_type": "markdown", "metadata": { "id": "87VzwDB4wJDB" }, "source": [ "Now, let's create a function to query the FAISS index and corresponding data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ma_uXO03vW3k", "outputId": "a61a356b-5d50-4cfc-dc4f-50d5dfb8ff1f" }, "outputs": [], "source": [ "def query(query_str):\n", " query = model.encode([query_str], normalize_embeddings=True)\n", " cosine, index = faiss_index.search(query, 1)\n", " print(f\"Cosine: {cosine[0][0]}\")\n", " loc, txt = vector_data[index[0][0]]\n", " print(f\"Location: {loc}\\nText:\\n\\n{txt}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's query in English to get German answers:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rSB2Kfk2vZjM" }, "outputs": [], "source": [ "# Translation: \"How do I roll back to a previous version?\"\n", "query(\"Wie kann ich auf eine frühere Version zurücksetzen?\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Translation: \"What does 'version control' mean?\"\n", "query(\"Was bedeutet 'Versionsverwaltung'?\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reversing the Roles: Querying German documents with English\r\n", "\n", "The book [*Pro Git: Everything You Need to Know About Git*](https://open.umn.edu/opentextbooks/textbooks/pro-git-everything-you-need-to-know-about-git) is also available in German. We can use this same model to give this demo with the languages reversed:\r\n", "\r\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Download the German edition:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!wget -O progit-de.epub https://open.umn.edu/opentextbooks/formats/3454" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Process the book the same way we did for English:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "book_data = decompose_epub(\"progit-de.epub\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we generate embeddings for the German version the same way we did for English:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "batch_size = 5\n", "\n", "vector_data = []\n", "faiss_index = faiss.IndexFlatIP(768)\n", "\n", "data = [(key, txt) for key, txt in book_data.items()]\n", "batches = [data[i:i + batch_size] for i in range(0, len(data), batch_size)]\n", "\n", "for ind, batch in enumerate(batches):\n", " print(f\"Processing batch {ind + 1} of {len(batches)}\")\n", " batch_embeddings = model.encode([x[1] for x in batch], normalize_embeddings=True)\n", " vector_data.extend(batch)\n", " faiss_index.add(batch_embeddings)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the same `query` function we used before, but with English questions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# The result should start with \"Was ist Versionsverwaltung?\"\n", "query(\"What is version control?\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying in Chinese\r\n", "The Chinese-English bilingual model works exactly the same way. To use the Chinese model instead, just run the following:\n", "\r\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from transformers import AutoModel\n", "\n", "model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-zh', trust_remote_code=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the Chinese edition of [*Pro Git: Everything You Need to Know About Git*](https://open.umn.edu/opentextbooks/textbooks/pro-git-everything-you-need-to-know-about-git):" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!wget -O progit-zh.epub https://open.umn.edu/opentextbooks/formats/3455" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Process the Chinese book like the German and English ones:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "book_data = decompose_epub(\"progit-zh.epub\")\n" ] }, { "cell_type": 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