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mhart/embeddinggemma-shortness.ipynb

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embeddinggemma-shortness.ipynb
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embeddinggemma-shortness.ipynb
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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/mhart/1d7fb7609d586b28e06d453f5597de24/embeddinggemma-shortness.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"## embeddinggemma prefers shorter content over more relevant content?\n",
"Model page: https://huggingface.co/google/embeddinggemma-300m\n",
"\n",
"An example of issues where embeddinggemma appears to prefer shorter content.\n",
"\n",
"**Is there some sort of padding issue going on? ie, padding tokens are causing the matching to be higher than just more tokens on the same topic?**"
],
"metadata": {
"id": "abq7_urtW37c"
}
},
{
"cell_type": "markdown",
"source": [
"The model you are trying to use is gated. Please make sure you have access to it by visiting the model page.To run inference, either set HF_TOKEN in your environment variables/ Secrets or run the following cell to login. 🤗"
],
"metadata": {
"id": "hbKjwbogW37e"
}
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import login\n",
"login(new_session=False)"
],
"metadata": {
"id": "e8svhlKeW37e",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "54582750-bd30-478d-cb1a-2efc71d01fc1"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: sentence-transformers[onnx] in /usr/local/lib/python3.12/dist-packages (5.1.0)\n",
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"Requirement already satisfied: flatbuffers in /usr/local/lib/python3.12/dist-packages (from onnxruntime>=1.11.0->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (25.2.10)\n",
"Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers[onnx]) (3.4.3)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers[onnx]) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers[onnx]) (2.5.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub>=0.20.0->sentence-transformers[onnx]) (2025.8.3)\n",
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"Requirement already satisfied: ml_dtypes in /usr/local/lib/python3.12/dist-packages (from onnx->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (0.5.3)\n",
"Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.12/dist-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (3.12.15)\n",
"Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.12/dist-packages (from coloredlogs->onnxruntime>=1.11.0->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (10.0)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (2025.2)\n",
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"Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (2.6.1)\n",
"Requirement already satisfied: aiosignal>=1.4.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (1.4.0)\n",
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (25.3.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (1.7.0)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (6.6.4)\n",
"Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (0.3.2)\n",
"Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.12/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (1.20.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas->datasets>=1.2.1->optimum[onnxruntime]>=1.23.1; extra == \"onnx\"->sentence-transformers[onnx]) (1.17.0)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install -U sentence-transformers\n",
"\n",
"from sentence_transformers import SentenceTransformer\n",
"\n",
"model = SentenceTransformer(\"google/embeddinggemma-300m\")\n",
"# model = SentenceTransformer(\"unsloth/embeddinggemma-300m-GGUF\")\n",
"# model = SentenceTransformer(\"onnx-community/embeddinggemma-300m-ONNX\", backend=\"onnx\",\n",
"# model_kwargs={\n",
"# \"provider\": \"CPUExecutionProvider\",\n",
"# \"file_name\": \"onnx/model.onnx\",\n",
"# })\n",
"\n",
"print(model)\n"
],
"metadata": {
"id": "UNAD7a3TW37f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 674,
"referenced_widgets": [
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]
},
"outputId": "fce5ad3b-825f-41a2-da85-a19fd7d8e339"
},
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
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{
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"name": "stdout",
"text": [
"SentenceTransformer(\n",
" (0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})\n",
" (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})\n",
" (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})\n",
" (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})\n",
" (4): Normalize()\n",
")\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# The first Queues Getting Started document is clearly the most relevant – but it's longer and has actual content about how to get started\n",
"# But the other three documents all score higher, even though they've got nothing to do with Queues\n",
"\n",
"# This happens with other documents and queries too\n",
"\n",
"docs = model.encode([\n",
" \"title: Queues · Getting started | text: Cloudflare Queues is a flexible messaging queue that allows you to queue messages for asynchronous processing. By following this guide, you will create your first queue, a Worker to publish messages to that queue, and a consumer Worker to consume messages from that queue.\\n\\n## Prerequisites\\n\\nTo use Queues, you will need:\\n\\n1. Sign up for a [Cloudflare account](https://dash.cloudflare.com/sign-up/workers-and-pages).\\n2. Install [`Node.js`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).\\n\\n### Node.js version manager\\n\\nUse a Node version manager like [Volta](https://volta.sh/) or [nvm](https://github.com/nvm-sh/nvm) to avoid permission issues and change Node.js versions. [Wrangler](/workers/wrangler/install-and-update/), discussed later in this guide, requires a Node version of `16.17.0` or later.\\n\\n## 1. Create a Worker project\\n\\nYou will access your queue from a Worker, the producer Worker. You must create at least one producer Worker to publish messages onto your queue. If you are using [R2 Bucket Event Notifications](/r2/buckets/event-notifications/), then you do not need a producer Worker.\\n\\nTo create a producer Worker, run:\\n\\n```sh\\nnpm create cloudflare@latest -- \\\"producer-worker\\n```\\n\\nThis will create a new directory, which will include both a `src/index.ts` Worker script, and a [`wrangler.jsonc`](/workers/wrangler/configuration/) configuration file. After you create your Worker, you will create a Queue to access.\\n\\nMove into the newly created directory:\\n\\n```sh\\ncd producer-worker\\n```\\n\\n## 2. Create a queue\\n\\nTo use queues, you need to create at least one queue to publish messages to and consume messages from.\\n\\nTo create a queue, run:\\n\\n```sh\\nnpx wrangler queues create \\u003CMY-QUEUE-NAME\\u003E\\n```\\n\",\n",
" \"title: Web3 · Ethereum Gateway · Concepts | text: As you get started with Cloudflare's Ethereum Gateway, you may want to read through the following concepts.\\n\\n:::note\\n\\nFor help with additional concepts, refer to the [Ethereum documentation](https://ethereum.org/).\\n:::\",\n",
" \"title: Load Balancing · Get started | text: Get started with load balancing in one of two ways:\\n\\n* [Quickstart](/load-balancing/get-started/quickstart/): Get up and running quickly with Load Balancing.\\n* [Learning path](/learning-paths/load-balancing/concepts/): Check an in-depth walkthrough for how to plan and set up a load balancer.\",\n",
" \"title: Workers · Tutorials · Build a Slackbot | text: If you want to get started building your own projects, review the existing list of [Quickstart templates](/workers/get-started/quickstarts/).\",\n",
"])\n",
"\n",
"query = \"How do I get started with Queues?\"\n",
"\n",
"print(model.similarity(docs, model.encode([f\"task: search result | query: {query}\"])))\n",
"\n",
"print(model.similarity(docs, model.encode([f\"task: question answering | query: {query}\"])))\n",
"\n",
"# tensor([[0.4166],\n",
"# [0.4299],\n",
"# [0.4668],\n",
"# [0.4799]])\n",
"# tensor([[0.3731],\n",
"# [0.4365],\n",
"# [0.4623],\n",
"# [0.4645]])"
],
"metadata": {
"id": "cjVMrSRSRH99",
"outputId": "bc6ea977-a29b-4c18-f0e7-d22ac72e98dd",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"tensor([[0.7392],\n",
" [0.4998],\n",
" [0.6326],\n",
" [0.5803]])\n",
"tensor([[0.7187],\n",
" [0.5220],\n",
" [0.6596],\n",
" [0.5953]])\n"
]
}
]
}
]
}
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