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"@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Embeddings" ], "metadata": { "id": "LGJrY0ujLMtF" } }, { "cell_type": "markdown", "source": [ "We have selected three distinct Transformer models to evaluate the trade-off between semantic understanding and computational efficiency for our recipe recommendation engine:\n", "\n", "**sentence-transformers/all-MiniLM-L6-v2** (The Baseline): Chosen for its extreme speed and compact size (80MB). It represents the industry standard for lightweight CPU-based inference, serving as our baseline for \"maximum efficiency.\"\n", "\n", "**sentence-transformers/all-mpnet-base-v2** (The Quality Benchmark): Chosen as the high-accuracy anchor. While significantly larger (420MB) and slower, it consistently ranks highest on semantic search benchmarks, allowing us to measure how much quality we might sacrifice for speed.\n", "\n", "**AAI/bge-small-en-v1.5**: Chosen as a potential \"best of both worlds\" solution. This newer model utilizes advanced pre-training techniques to achieve accuracy comparable to MPNet while maintaining a small footprint (133MB) similar to MiniLM, making it a strong candidate for optimal performance." ], "metadata": { "id": "7SCUN2BrM-Lm" } }, { "cell_type": "markdown", "source": [], "metadata": { "id": "3CSwRaE1NGG3" } }, { "cell_type": "code", "source": [ "import time\n", "import pandas as pd\n", "import torch\n", "from sentence_transformers import SentenceTransformer, util\n", "from google.colab import files\n", "import os\n" ], "metadata": { "id": "-Vip8kh7NLG_" }, "execution_count": 7, "outputs": [] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "\n", "print(\"Please upload 'RecipeData_10K.csv' from your computer:\")\n", "uploaded = files.upload()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 88 }, "id": "-fXgjpuDN787", "outputId": "da3e2d5c-f9e5-44dd-9072-276548bf60f4" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Please upload 'RecipeData_10K.csv' from your computer:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving RecipeData_10K.csv to RecipeData_10K (1).csv\n" ] } ] }, { "cell_type": "code", "source": [ "# --- CONFIGURATION ---\n", "FILE_NAME = 'RecipeData_10K.csv'\n", "QUERY = \"I want a creamy italian rice dish with mushrooms\"\n", "SAMPLE_SIZE = 2000 # We test on 2,000 recipes for quick evaluation. Set to 10000 for full run.\n", "\n", "# --- 1. LOAD DATASET ---\n", "print(f\" Loading {FILE_NAME}...\")\n", "try:\n", " df = pd.read_csv(FILE_NAME)\n", " # We use 'Raw_Output' because it contains Title + Ingredients + Instructions\n", " data = df['Raw_Output'].astype(str).tolist()[:SAMPLE_SIZE]\n", " titles = df['Title'].astype(str).tolist()[:SAMPLE_SIZE]\n", " print(f\" Loaded {len(data)} recipes for evaluation.\\n\")\n", "except Exception as e:\n", " print(f\" Error loading file: {e}\")\n", " data = []" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MdK1sNwbNUsA", "outputId": "ff49d286-4074-4982-e6d0-21d05be0a785" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Loading RecipeData_10K.csv...\n", " Loaded 2000 recipes for evaluation.\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "# --- 2. DEFINE MODELS ---\n", "models = [\n", " 'sentence-transformers/all-MiniLM-L6-v2', # Speed Baseline\n", " 'sentence-transformers/all-mpnet-base-v2', # Accuracy Baseline\n", " 'BAAI/bge-small-en-v1.5' # Modern Efficient\n", "]\n", "\n", "results = []\n", "\n", "if data:\n", " print(f\"{'MODEL':<40} | {'TIME':<8} | {'SIZE':<8} | {'SCORE':<6} | {'MATCH'}\")\n", " print(\"-\" * 110)\n", "\n", " for model_name in models:\n", " # Load Model\n", " load_start = time.time()\n", " model = SentenceTransformer(model_name)\n", "\n", " # Calculate Model Size (MB)\n", " params = sum(p.numel() for p in model.parameters())\n", " size_mb = params * 4 / (1024 * 1024)\n", "\n", " # Measure Encoding Time\n", " enc_start = time.time()\n", " corpus_embeddings = model.encode(data, convert_to_tensor=True, show_progress_bar=False)\n", " enc_time = time.time() - enc_start\n", "\n", " # Measure Retrieval Quality (Cosine Similarity)\n", " # BGE models benefit from a specific instruction for queries\n", " if 'bge' in model_name:\n", " q_embedding = model.encode(f\"Represent this sentence for searching relevant recipes: {QUERY}\", convert_to_tensor=True)\n", " else:\n", " q_embedding = model.encode(QUERY, convert_to_tensor=True)\n", "\n", " hits = util.cos_sim(q_embedding, corpus_embeddings)[0]\n", "\n", " # Get Best Match\n", " best_score_idx = torch.argmax(hits).item()\n", " best_score = hits[best_score_idx].item()\n", " best_match_title = titles[best_score_idx]\n", "\n", " # Print Row\n", " print(f\"{model_name:<40} | {enc_time:.2f}s | {size_mb:.0f} MB | {best_score:.3f} | {best_match_title[:35]}...\")\n", "\n", " results.append({\n", " \"Model\": model_name,\n", " \"Encoding Time (s)\": enc_time,\n", " \"Size (MB)\": size_mb,\n", " \"Similarity Score\": best_score,\n", " \"Best Match\": best_match_title\n", " })\n", "\n", " # --- 3. FINAL RESULTS TABLE ---\n", " print(\"\\n\" + \"=\"*40)\n", " print(\"FINAL RESULTS TABLE\")\n", " print(\"=\"*40)\n", " res_df = pd.DataFrame(results)\n", " print(res_df[['Model', 'Encoding Time (s)', 'Size (MB)', 'Similarity Score', 'Best Match']].to_string(index=False))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "095d4a0164d741babfbfdc7cb31a6a94", "672270755b1c4e628e89308d06924482", "06435392cf054b0cb6724c6fdc16781e", "489fba3e70ac49c3bfdbb962c4853e79", "fdf440bfeaa2468a9e82b0d2c4bbd157", "16cd54f533f44393b47e2f408050cbba", "3237f3d5e33947ef8f8ab9d98184e604", "da735b4276e64dc6a5ed21de35b100d3", "5979e11fde044af189da136a16593c0e", "cd60d1d12fa7481f9288b9d6cfc46dc5", "2579c53527024f9b809815c9f9c4edd5", "8fd71cc5d9514c3e84d6d475f3336a4a", "fb50071c494c4723adbb69411464f173", "5a3ed2ba9ceb4f3a880c312692951da9", "b271a1e969574c69a0ec8c068135b712", "c0ddcd3e51484dce822a799db309bbad", "e3f7b93c007e4ff5a1defb3ff8093323", 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MATCH\n", "--------------------------------------------------------------------------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/349 [00:00