Llama3_Embeddings_classify DEMO.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install necessary packages\n",
    "```bash\n",
    "    !pip install ollama\n",
    "    !pip install datasets\n",
    "    !pip install -U scikit-learn\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "imdb = load_dataset(\"imdb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_train_pd = imdb[\"train\"].to_pandas()\n",
    "imdb_test_pd = imdb[\"test\"].to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I rented I AM CURIOUS-YELLOW from my video sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"I Am Curious: Yellow\" is a risible and preten...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>If only to avoid making this type of film in t...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>This film was probably inspired by Godard's Ma...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oh, brother...after hearing about this ridicul...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>A hit at the time but now better categorised a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>I love this movie like no other. Another time ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>This film and it's sequel Barry Mckenzie holds...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>'The Adventures Of Barry McKenzie' started lif...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>The story centers around Barry McKenzie who mu...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label\n",
       "0      I rented I AM CURIOUS-YELLOW from my video sto...      0\n",
       "1      \"I Am Curious: Yellow\" is a risible and preten...      0\n",
       "2      If only to avoid making this type of film in t...      0\n",
       "3      This film was probably inspired by Godard's Ma...      0\n",
       "4      Oh, brother...after hearing about this ridicul...      0\n",
       "...                                                  ...    ...\n",
       "24995  A hit at the time but now better categorised a...      1\n",
       "24996  I love this movie like no other. Another time ...      1\n",
       "24997  This film and it's sequel Barry Mckenzie holds...      1\n",
       "24998  'The Adventures Of Barry McKenzie' started lif...      1\n",
       "24999  The story centers around Barry McKenzie who mu...      1\n",
       "\n",
       "[25000 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_train_pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting 10 for faster processing\n",
    "samples_per_label = 10 // imdb_train_pd['label'].nunique()\n",
    "df_small_train_sample = imdb_train_pd.groupby('label').apply(lambda x: x.sample(samples_per_label)).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This movie is like the material S.E. Hinton wa...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RUN...do not walk away from this movie!!!!! Ai...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dieter Bohlen, Germany's notorious composer an...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>If I had not read Pat Barker's 'Union Street' ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The explosion of TV channels must be eternally...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>This is Jackie Chan's best film, and my person...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>What I loved about the on-screen adaptation of...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>This movie is perfect for all the romantics in...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I bought this movie a few days ago, and though...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This is a very funny movie, easy to watch, tha...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label\n",
       "0  This movie is like the material S.E. Hinton wa...      0\n",
       "1  RUN...do not walk away from this movie!!!!! Ai...      0\n",
       "2  Dieter Bohlen, Germany's notorious composer an...      0\n",
       "3  If I had not read Pat Barker's 'Union Street' ...      0\n",
       "4  The explosion of TV channels must be eternally...      0\n",
       "5  This is Jackie Chan's best film, and my person...      1\n",
       "6  What I loved about the on-screen adaptation of...      1\n",
       "7  This movie is perfect for all the romantics in...      1\n",
       "8  I bought this movie a few days ago, and though...      1\n",
       "9  This is a very funny movie, easy to watch, tha...      1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_small_train_sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Convert Text To Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ollama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming small_train_sample is your DataFrame\n",
    "df = df_small_train_sample\n",
    "\n",
    "# Define a function to generate embeddings\n",
    "def generate_embeddings(row,text_column_name='text'):\n",
    "    embeddings = ollama.embeddings(\n",
    "        model='llama3',\n",
    "        prompt=row[text_column_name],\n",
    "    )\n",
    "    return embeddings[\"embedding\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the column name\n",
    "column_name = 'text'\n",
    "\n",
    "# Apply the function to the specified column and store the result in a new column 'embeddings'\n",
    "df['embeddings'] = df.apply(lambda row: generate_embeddings(row, column_name), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This movie is like the material S.E. Hinton wa...</td>\n",
       "      <td>0</td>\n",
       "      <td>[-1.3971306085586548, -2.118555784225464, 0.04...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RUN...do not walk away from this movie!!!!! Ai...</td>\n",
       "      <td>0</td>\n",
       "      <td>[-2.844423770904541, 0.012460770085453987, 1.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dieter Bohlen, Germany's notorious composer an...</td>\n",
       "      <td>0</td>\n",
       "      <td>[-1.0280311107635498, -1.0665245056152344, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>If I had not read Pat Barker's 'Union Street' ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[-2.0157930850982666, 0.15467536449432373, -2....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The explosion of TV channels must be eternally...</td>\n",
       "      <td>0</td>\n",
       "      <td>[-1.297585129737854, -2.260007381439209, -1.45...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>This is Jackie Chan's best film, and my person...</td>\n",
       "      <td>1</td>\n",
       "      <td>[-2.6929218769073486, 0.6353673338890076, 1.17...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>What I loved about the on-screen adaptation of...</td>\n",
       "      <td>1</td>\n",
       "      <td>[-0.7430300116539001, -0.21735097467899323, 1....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>This movie is perfect for all the romantics in...</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.6251738667488098, -2.127596378326416, 1.711...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I bought this movie a few days ago, and though...</td>\n",
       "      <td>1</td>\n",
       "      <td>[-3.6958141326904297, 0.06789332628250122, -3....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This is a very funny movie, easy to watch, tha...</td>\n",
       "      <td>1</td>\n",
       "      <td>[-1.786821961402893, -1.2234193086624146, 1.91...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label  \\\n",
       "0  This movie is like the material S.E. Hinton wa...      0   \n",
       "1  RUN...do not walk away from this movie!!!!! Ai...      0   \n",
       "2  Dieter Bohlen, Germany's notorious composer an...      0   \n",
       "3  If I had not read Pat Barker's 'Union Street' ...      0   \n",
       "4  The explosion of TV channels must be eternally...      0   \n",
       "5  This is Jackie Chan's best film, and my person...      1   \n",
       "6  What I loved about the on-screen adaptation of...      1   \n",
       "7  This movie is perfect for all the romantics in...      1   \n",
       "8  I bought this movie a few days ago, and though...      1   \n",
       "9  This is a very funny movie, easy to watch, tha...      1   \n",
       "\n",
       "                                          embeddings  \n",
       "0  [-1.3971306085586548, -2.118555784225464, 0.04...  \n",
       "1  [-2.844423770904541, 0.012460770085453987, 1.7...  \n",
       "2  [-1.0280311107635498, -1.0665245056152344, 0.0...  \n",
       "3  [-2.0157930850982666, 0.15467536449432373, -2....  \n",
       "4  [-1.297585129737854, -2.260007381439209, -1.45...  \n",
       "5  [-2.6929218769073486, 0.6353673338890076, 1.17...  \n",
       "6  [-0.7430300116539001, -0.21735097467899323, 1....  \n",
       "7  [0.6251738667488098, -2.127596378326416, 1.711...  \n",
       "8  [-3.6958141326904297, 0.06789332628250122, -3....  \n",
       "9  [-1.786821961402893, -1.2234193086624146, 1.91...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Let's Classify Using Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Assuming df is your DataFrame, 'embeddings' is your feature column and 'label' is your target column\n",
    "X_train = df[\"embeddings\"].tolist()\n",
    "y_train = df['label']\n",
    "\n",
    "\n",
    "# Initialize the KNN classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "knn.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You'll need a separate test set (X_test and y_test) to evaluate the model\n",
    "# y_pred = knn.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy of the model\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# print(f'Accuracy: {accuracy}')"
   ]
  }
 ],
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