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"source": [
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],
"metadata": {
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},
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]
}
]
},
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"source": [
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],
"metadata": {
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}
},
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"source": [
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],
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},
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},
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"clothing_data = df[['url', 'product']].copy()\n",
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],
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" \n",
" 2 | \n",
" https://www.amazon.com/dp/B09LYF2ST7 | \n",
" BERIBES Bluetooth Headphones Over Ear, 65H Pla... | \n",
"
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" \n",
" 3 | \n",
" https://www.amazon.com/dp/B08FCGH2RL | \n",
" Skullcandy Crusher Evo Wireless Over-Ear Bluet... | \n",
"
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" \n",
" 4 | \n",
" https://www.amazon.com/dp/B000AJIF4E | \n",
" Sony MDR7506 Professional Large Diaphragm Head... | \n",
"
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" "
]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"source": [
"## Preprocessing"
],
"metadata": {
"id": "bUjHcxOsbLhI"
}
},
{
"cell_type": "code",
"source": [
"import nltk\n",
"import string\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem import WordNetLemmatizer\n",
"\n",
"nltk.download('punkt')\n",
"nltk.download('stopwords')\n",
"nltk.download('wordnet')\n",
"\n",
"def preprocess_text(text):\n",
" # Tokenize the text into individual words\n",
" tokens = word_tokenize(text)\n",
" \n",
" # Lowercase the tokens\n",
" tokens = [token.lower() for token in tokens]\n",
" \n",
" # Remove special characters and punctuation\n",
" tokens = [token.translate(str.maketrans('', '', string.punctuation)) for token in tokens]\n",
" \n",
" # Remove stopwords\n",
" stop_words = set(stopwords.words('english'))\n",
" tokens = [token for token in tokens if token not in stop_words]\n",
" \n",
" # Lemmatize the tokens\n",
" lemmatizer = WordNetLemmatizer()\n",
" tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
" \n",
" # Join the tokens back into a single string\n",
" preprocessed_text = ' '.join(tokens)\n",
" \n",
" return preprocessed_text\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PG2B6YlzZj_q",
"outputId": "bba38d9c-99b9-4bca-b887-803cbde8fa2c"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Unzipping tokenizers/punkt.zip.\n",
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n",
"[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"preprocessed_products = []\n",
"for index, row in clothing_data.iterrows():\n",
" preprocessed_product = preprocess_text(row['product'])\n",
" preprocessed_products.append(preprocessed_product)\n",
"\n",
"# Add the preprocessed text to a new column in the clothing_data\n",
"clothing_data['preprocessed_product'] = preprocessed_products\n"
],
"metadata": {
"id": "L7XJcLqjbRDg"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"clothing_data.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "pKNoqZbibnHm",
"outputId": "e4637012-f330-4d44-840b-1b514909e70d"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" url \\\n",
"0 https://www.amazon.com/dp/B09BVXT8TJ \n",
"1 https://www.amazon.com/dp/B094242FV1 \n",
"2 https://www.amazon.com/dp/B09LYF2ST7 \n",
"3 https://www.amazon.com/dp/B08FCGH2RL \n",
"4 https://www.amazon.com/dp/B000AJIF4E \n",
"\n",
" product \\\n",
"0 SHOKZ OpenRun Pro - Open-Ear Bluetooth Bone Co... \n",
"1 SAMSUNG Galaxy Buds 2 True Wireless Earbuds No... \n",
"2 BERIBES Bluetooth Headphones Over Ear, 65H Pla... \n",
"3 Skullcandy Crusher Evo Wireless Over-Ear Bluet... \n",
"4 Sony MDR7506 Professional Large Diaphragm Head... \n",
"\n",
" preprocessed_product \n",
"0 shokz openrun pro openear bluetooth bone cond... \n",
"1 samsung galaxy bud 2 true wireless earbuds noi... \n",
"2 beribes bluetooth headphone ear 65h playtime ... \n",
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"4 sony mdr7506 professional large diaphragm head... "
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" product | \n",
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" \n",
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" 0 | \n",
" https://www.amazon.com/dp/B09BVXT8TJ | \n",
" SHOKZ OpenRun Pro - Open-Ear Bluetooth Bone Co... | \n",
" shokz openrun pro openear bluetooth bone cond... | \n",
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\n",
" \n",
" 1 | \n",
" https://www.amazon.com/dp/B094242FV1 | \n",
" SAMSUNG Galaxy Buds 2 True Wireless Earbuds No... | \n",
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"
\n",
" \n",
" 2 | \n",
" https://www.amazon.com/dp/B09LYF2ST7 | \n",
" BERIBES Bluetooth Headphones Over Ear, 65H Pla... | \n",
" beribes bluetooth headphone ear 65h playtime ... | \n",
"
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" \n",
" 3 | \n",
" https://www.amazon.com/dp/B08FCGH2RL | \n",
" Skullcandy Crusher Evo Wireless Over-Ear Bluet... | \n",
" skullcandy crusher evo wireless overear blueto... | \n",
"
\n",
" \n",
" 4 | \n",
" https://www.amazon.com/dp/B000AJIF4E | \n",
" Sony MDR7506 Professional Large Diaphragm Head... | \n",
" sony mdr7506 professional large diaphragm head... | \n",
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},
{
"cell_type": "code",
"source": [
"clothing_data.to_csv('clothing_data_preprocessed.csv', encoding = 'utf-8-sig')"
],
"metadata": {
"id": "tK-wU9kH7PdI"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"pip install -U sentence-transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LgI0jxIdq6uE",
"outputId": "9318784d-b637-47a0-a389-aceed7b6872d"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting sentence-transformers\n",
" Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Collecting transformers<5.0.0,>=4.6.0 (from sentence-transformers)\n",
" Downloading transformers-4.29.2-py3-none-any.whl (7.1 MB)\n",
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"Collecting sentencepiece (from sentence-transformers)\n",
" Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m63.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers)\n",
" Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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"Building wheels for collected packages: sentence-transformers\n",
" Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125926 sha256=d9fad114a55e72e501b610fb5f33ae97bebc597e271fbe14d9b91e1d7ad8fee6\n",
" Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
"Successfully built sentence-transformers\n",
"Installing collected packages: tokenizers, sentencepiece, huggingface-hub, transformers, sentence-transformers\n",
"Successfully installed huggingface-hub-0.14.1 sentence-transformers-2.2.2 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.29.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sentence_transformers import SentenceTransformer, util\n",
"model = SentenceTransformer('all-MiniLM-L6-v2')\n",
"embeddings = model.encode(clothing_data['preprocessed_product'], convert_to_tensor=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 465,
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"id": "Z6a9MNifimVI",
"outputId": "baf8a01b-48d2-41f3-8a0e-7837856c578f"
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"execution_count": 10,
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{
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},
{
"cell_type": "markdown",
"source": [
"## Save the whole data embeddings in npy file"
],
"metadata": {
"id": "nuposoHAzNJA"
}
},
{
"cell_type": "code",
"source": [
"# Move the embeddings tensor from GPU to CPU\n",
"import numpy as np\n",
"embeddings_cpu = embeddings.cpu()\n",
"\n",
"# Convert the CPU tensor to a NumPy array\n",
"embeddings_array = embeddings_cpu.numpy()\n",
"\n",
"# Save the NumPy array to file\n",
"np.save('embeddingsnpy', embeddings_array)"
],
"metadata": {
"id": "QJeZj0y-5u1O"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Similarity Search"
],
"metadata": {
"id": "e9EfFAdWzS-r"
}
},
{
"cell_type": "code",
"source": [
"query = \"Men's jeans black and comfortable\"\n",
"query_embedding = model.encode([query], convert_to_tensor=True)\n",
"similarity_scores = util.pytorch_cos_sim(query_embedding, embeddings)[0]\n",
"sorted_indices = similarity_scores.argsort(descending=True)\n",
"\n",
"top_k = 5\n",
"similar_indices = sorted_indices[:top_k].cpu().numpy()\n",
"similar_urls = clothing_data.loc[similar_indices, 'url']"
],
"metadata": {
"id": "b_lMiSdZisG2"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"similar_urls"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fv9vKSDei309",
"outputId": "e54c16cf-56d5-47fb-e965-f2d8e2398f45"
},
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"2495 https://www.amazon.in/dp/B0BWNB6D36\n",
"2428 https://www.amazon.in/dp/B0C24BH9ZF\n",
"2302 https://www.amazon.in/dp/B09B4CFP89\n",
"2362 https://www.amazon.in/dp/B0BFF5RMPX\n",
"2264 https://www.amazon.in/dp/B08KWFRY6W\n",
"Name: url, dtype: object"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "cgZWPjcfMy5S"
},
"execution_count": null,
"outputs": []
}
]
}