Upload scikit-learn-iris-embeddings-eb70c965-7ce7-4a3c-998a-a2d43d473aee.ipynb with huggingface_hub

scikit-learn-iris-embeddings-eb70c965-7ce7-4a3c-998a-a2d43d473aee.ipynb

@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3e1ee4bf",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# **Embeddings Notebook for scikit-learn/iris dataset**\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc5ce52a",
+   "metadata": {},
+   "source": [
+    "## 1. Setup necessary libraries and load the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e652f2d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install and import necessary libraries.\n",
+    "!pip install pandas sentence-transformers faiss-cpu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8017f20d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sentence_transformers import SentenceTransformer\n",
+    "import faiss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e57d5df6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the dataset as a DataFrame\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"hf://datasets/scikit-learn/iris/Iris.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ee9c698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify the column name that contains the text data to generate embeddings\n",
+    "column_to_generate_embeddings = 'Species'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91336c0d",
+   "metadata": {},
+   "source": [
+    "## 2. Loading embedding model and creating FAISS index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa97506e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove duplicate entries based on the specified column\n",
+    "df = df.drop_duplicates(subset=column_to_generate_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e2720b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the column data to a list of text entries\n",
+    "text_list = df[column_to_generate_embeddings].tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "740a51ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify the embedding model you want to use\n",
+    "model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2230da68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vectors = model.encode(text_list)\n",
+    "vector_dimension = vectors.shape[1]\n",
+    "\n",
+    "# Initialize the FAISS index with the appropriate dimension (384 for this model)\n",
+    "index = faiss.IndexFlatL2(vector_dimension)\n",
+    "\n",
+    "# Encode the text list into embeddings and add them to the FAISS index\n",
+    "index.add(vectors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5989dc76",
+   "metadata": {},
+   "source": [
+    "## 3. Perform a text search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69c0e3ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify the text you want to search for in the list\n",
+    "text_to_search = text_list[0]\n",
+    "print(f\"Text to search: {text_to_search}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fdbe2b69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate the embedding for the search query\n",
+    "query_embedding = model.encode([text_to_search])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1bda94a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Perform the search to find the 'k' nearest neighbors (adjust 'k' as needed)\n",
+    "D, I = index.search(query_embedding, k=10)\n",
+    "\n",
+    "# Print the similar documents\n",
+    "print(f\"Similar documents: {[text_list[i] for i in I[0]]}\")"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}