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Hands-On-Large-Language-Models/chapter02/Chapter 2 - Tokens and Token Embeddings.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "g_a9QvUFVCUR"
},
"source": [
"<h1>Chapter 2 - Tokens and Token Embeddings</h1>\n",
"<i>Exploring tokens and embeddings as an integral part of building LLMs</i>\n",
"\n",
"\n",
"<a href=\"https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961\"><img src=\"https://img.shields.io/badge/Buy%20the%20Book!-grey?logo=amazon\"></a>\n",
"<a href=\"https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/\"><img src=\"https://img.shields.io/badge/O'Reilly-white.svg?logo=data:image/svg%2bxml;base64,PHN2ZyB3aWR0aD0iMzQiIGhlaWdodD0iMjciIHZpZXdCb3g9IjAgMCAzNCAyNyIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGNpcmNsZSBjeD0iMTMiIGN5PSIxNCIgcj0iMTEiIHN0cm9rZT0iI0Q0MDEwMSIgc3Ryb2tlLXdpZHRoPSI0Ii8+CjxjaXJjbGUgY3g9IjMwLjUiIGN5PSIzLjUiIHI9IjMuNSIgZmlsbD0iI0Q0MDEwMSIvPgo8L3N2Zz4K\"></a>\n",
"<a href=\"https://github.com/HandsOnLLM/Hands-On-Large-Language-Models\"><img src=\"https://img.shields.io/badge/GitHub%20Repository-black?logo=github\"></a>\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ZiM0ykgAzFamSGetoDd8TzMP7DXrWPEh?usp=sharing)\n",
"\n",
"---\n",
"\n",
"This notebook is for Chapter 2 of the [Hands-On Large Language Models](https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961) book by [Jay Alammar](https://www.linkedin.com/in/jalammar) and [Maarten Grootendorst](https://www.linkedin.com/in/mgrootendorst/).\n",
"\n",
"---\n",
"\n",
"<a href=\"https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961\">\n",
"<img src=\"https://raw.githubusercontent.com/HandsOnLLM/Hands-On-Large-Language-Models/main/images/book_cover.png\" width=\"350\"/></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oQHfpqT_t9-K"
},
"source": [
"# Downloading and Running An LLM\n",
"\n",
"The first step is to load our model onto the GPU for faster inference. Note that we load the model and tokenizer separately and keep them as such so that we can explore them separately."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
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"base_uri": "https://localhost:8080/",
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]
},
"executionInfo": {
"elapsed": 95520,
"status": "ok",
"timestamp": 1723034396041,
"user": {
"displayName": "Jay Alammar جهاد العمار",
"userId": "14617748739431919458"
},
"user_tz": -60
},
"id": "jjU8NBHnwA4j",
"outputId": "286bdccb-f25d-4b0e-bda3-44d2a4be45cd"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"`flash-attention` package not found, consider installing for better performance: No module named 'flash_attn'.\n",
"Current `flash-attention` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dd45dd8837f94b38ae6f4ffd205d9ea6",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"\n",
"# Load model and tokenizer\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" \"microsoft/Phi-3-mini-4k-instruct\",\n",
" device_map=\"cuda\",\n",
" torch_dtype=\"auto\",\n",
" trust_remote_code=True,\n",
")\n",
"tokenizer = AutoTokenizer.from_pretrained(\"microsoft/Phi-3-mini-4k-instruct\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 5750,
"status": "ok",
"timestamp": 1719641447389,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "_iVl5yePuq3B",
"outputId": "4ce629bf-3897-4ab0-8cf1-8f55e2040155"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:transformers_modules.microsoft.Phi-3-mini-4k-instruct.ff07dc01615f8113924aed013115ab2abd32115b.modeling_phi3:You are not running the flash-attention implementation, expect numerical differences.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<s> Write an email apologizing to Sarah for the tragic gardening mishap. Explain how it happened.<|assistant|> Subject: My Sincere Apologies for the Gardening Mishap\n",
"\n",
"Dear\n"
]
}
],
"source": [
"prompt = \"Write an email apologizing to Sarah for the tragic gardening mishap. Explain how it happened.<|assistant|>\"\n",
"\n",
"# Tokenize the input prompt\n",
"input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(\"cuda\")\n",
"\n",
"# Generate the text\n",
"generation_output = model.generate(\n",
" input_ids=input_ids,\n",
" max_new_tokens=20\n",
")\n",
"\n",
"# Print the output\n",
"print(tokenizer.decode(generation_output[0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 4,
"status": "ok",
"timestamp": 1719641447389,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "JmzgbbdKuvHt",
"outputId": "82511d5b-7949-49a0-e3a6-c128564575c8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 1, 14350, 385, 4876, 27746, 5281, 304, 19235, 363, 278,\n",
" 25305, 293, 16423, 292, 286, 728, 481, 29889, 12027, 7420,\n",
" 920, 372, 9559, 29889, 32001]], device='cuda:0')\n"
]
}
],
"source": [
"print(input_ids)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1719641447389,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "W4vsjbxwu1K1",
"outputId": "506f32d1-f058-4cfd-a9cd-13c4dabe80e6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<s>\n",
"Write\n",
"an\n",
"email\n",
"apolog\n",
"izing\n",
"to\n",
"Sarah\n",
"for\n",
"the\n",
"trag\n",
"ic\n",
"garden\n",
"ing\n",
"m\n",
"ish\n",
"ap\n",
".\n",
"Exp\n",
"lain\n",
"how\n",
"it\n",
"happened\n",
".\n",
"<|assistant|>\n"
]
}
],
"source": [
"for id in input_ids[0]:\n",
" print(tokenizer.decode(id))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1719641447389,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "A9wRZ3J3u4z1",
"outputId": "7efaa49c-7a5a-41d7-f000-7aace16007e5"
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[ 1, 14350, 385, 4876, 27746, 5281, 304, 19235, 363, 278,\n",
" 25305, 293, 16423, 292, 286, 728, 481, 29889, 12027, 7420,\n",
" 920, 372, 9559, 29889, 32001, 3323, 622, 29901, 1619, 317,\n",
" 3742, 406, 6225, 11763, 363, 278, 19906, 292, 341, 728,\n",
" 481, 13, 13, 29928, 799]], device='cuda:0')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generation_output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 275,
"status": "ok",
"timestamp": 1723034447362,
"user": {
"displayName": "Jay Alammar جهاد العمار",
"userId": "14617748739431919458"
},
"user_tz": -60
},
"id": "7QlHLof3u8A3",
"outputId": "c2315e1b-91b4-4a1b-9bcc-084f16ac8db1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sub\n",
"ject\n",
"Subject\n",
":\n"
]
}
],
"source": [
"print(tokenizer.decode(3323))\n",
"print(tokenizer.decode(622))\n",
"print(tokenizer.decode([3323, 622]))\n",
"print(tokenizer.decode(29901))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T9nRducW48bd"
},
"source": [
"# Comparing Trained LLM Tokenizers\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7W0xFIVo5A0S"
},
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"\n",
"colors_list = [\n",
" '102;194;165', '252;141;98', '141;160;203',\n",
" '231;138;195', '166;216;84', '255;217;47'\n",
"]\n",
"\n",
"def show_tokens(sentence, tokenizer_name):\n",
" tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)\n",
" token_ids = tokenizer(sentence).input_ids\n",
" for idx, t in enumerate(token_ids):\n",
" print(\n",
" f'\\x1b[0;30;48;2;{colors_list[idx % len(colors_list)]}m' +\n",
" tokenizer.decode(t) +\n",
" '\\x1b[0m',\n",
" end=' '\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gcc3JjwX5DK-"
},
"outputs": [],
"source": [
"text = \"\"\"\n",
"English and CAPITALIZATION\n",
"🎵 鸟\n",
"show_tokens False None elif == >= else: two tabs:\" \" Three tabs: \" \"\n",
"12.0*50=600\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 354,
"status": "ok",
"timestamp": 1725544666773,
"user": {
"displayName": "Jay Alammar جهاد العمار",
"userId": "14617748739431919458"
},
"user_tz": 240
},
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]
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"\u001b[0;30;48;2;102;194;165mEnglish\u001b[0m \u001b[0;30;48;2;252;141;98mand\u001b[0m \u001b[0;30;48;2;141;160;203mCA\u001b[0m \u001b[0;30;48;2;231;138;195mPI\u001b[0m \u001b[0;30;48;2;166;216;84mTAL\u001b[0m \u001b[0;30;48;2;255;217;47mIZ\u001b[0m \u001b[0;30;48;2;102;194;165mATION\u001b[0m \u001b[0;30;48;2;252;141;98m\u001b[0m \u001b[0;30;48;2;141;160;203m<unk>\u001b[0m \u001b[0;30;48;2;231;138;195m\u001b[0m \u001b[0;30;48;2;166;216;84m<unk>\u001b[0m \u001b[0;30;48;2;255;217;47mshow\u001b[0m \u001b[0;30;48;2;102;194;165m_\u001b[0m \u001b[0;30;48;2;252;141;98mto\u001b[0m \u001b[0;30;48;2;141;160;203mken\u001b[0m \u001b[0;30;48;2;231;138;195ms\u001b[0m \u001b[0;30;48;2;166;216;84mFal\u001b[0m \u001b[0;30;48;2;255;217;47ms\u001b[0m \u001b[0;30;48;2;102;194;165me\u001b[0m \u001b[0;30;48;2;252;141;98mNone\u001b[0m \u001b[0;30;48;2;141;160;203m\u001b[0m \u001b[0;30;48;2;231;138;195me\u001b[0m \u001b[0;30;48;2;166;216;84ml\u001b[0m \u001b[0;30;48;2;255;217;47mif\u001b[0m \u001b[0;30;48;2;102;194;165m=\u001b[0m \u001b[0;30;48;2;252;141;98m=\u001b[0m \u001b[0;30;48;2;141;160;203m>\u001b[0m \u001b[0;30;48;2;231;138;195m=\u001b[0m \u001b[0;30;48;2;166;216;84melse\u001b[0m \u001b[0;30;48;2;255;217;47m:\u001b[0m \u001b[0;30;48;2;102;194;165mtwo\u001b[0m \u001b[0;30;48;2;252;141;98mtab\u001b[0m \u001b[0;30;48;2;141;160;203ms\u001b[0m \u001b[0;30;48;2;231;138;195m:\u001b[0m \u001b[0;30;48;2;166;216;84m\"\u001b[0m \u001b[0;30;48;2;255;217;47m\"\u001b[0m \u001b[0;30;48;2;102;194;165mThree\u001b[0m \u001b[0;30;48;2;252;141;98mtab\u001b[0m \u001b[0;30;48;2;141;160;203ms\u001b[0m \u001b[0;30;48;2;231;138;195m:\u001b[0m \u001b[0;30;48;2;166;216;84m\"\u001b[0m \u001b[0;30;48;2;255;217;47m\"\u001b[0m \u001b[0;30;48;2;102;194;165m12.\u001b[0m \u001b[0;30;48;2;252;141;98m0\u001b[0m \u001b[0;30;48;2;141;160;203m*\u001b[0m \u001b[0;30;48;2;231;138;195m50\u001b[0m \u001b[0;30;48;2;166;216;84m=\u001b[0m \u001b[0;30;48;2;255;217;47m600\u001b[0m \u001b[0;30;48;2;102;194;165m\u001b[0m \u001b[0;30;48;2;252;141;98m</s>\u001b[0m "
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"show_tokens(text, \"google/flan-t5-small\")"
]
},
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"id": "1ymhAsTg5H5e",
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"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
},
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],
"source": [
"# The official is `tiktoken` but this the same tokenizer on the HF platform\n",
"show_tokens(text, \"Xenova/gpt-4\")"
]
},
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"special_tokens_map.json: 0%| | 0.00/958 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
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"\u001b[0;30;48;2;102;194;165m\n",
"\u001b[0m \u001b[0;30;48;2;252;141;98mEnglish\u001b[0m \u001b[0;30;48;2;141;160;203m and\u001b[0m \u001b[0;30;48;2;231;138;195m CAPITAL\u001b[0m \u001b[0;30;48;2;166;216;84mIZATION\u001b[0m \u001b[0;30;48;2;255;217;47m\n",
"\u001b[0m \u001b[0;30;48;2;102;194;165m<35>\u001b[0m \u001b[0;30;48;2;252;141;98m<38>\u001b[0m \u001b[0;30;48;2;141;160;203m<33>\u001b[0m \u001b[0;30;48;2;231;138;195m \u001b[0m \u001b[0;30;48;2;166;216;84m<34>\u001b[0m \u001b[0;30;48;2;255;217;47m<37>\u001b[0m \u001b[0;30;48;2;102;194;165m\n",
"\u001b[0m \u001b[0;30;48;2;252;141;98mshow\u001b[0m \u001b[0;30;48;2;141;160;203m_\u001b[0m \u001b[0;30;48;2;231;138;195mtokens\u001b[0m \u001b[0;30;48;2;166;216;84m False\u001b[0m \u001b[0;30;48;2;255;217;47m None\u001b[0m \u001b[0;30;48;2;102;194;165m elif\u001b[0m \u001b[0;30;48;2;252;141;98m ==\u001b[0m \u001b[0;30;48;2;141;160;203m >=\u001b[0m \u001b[0;30;48;2;231;138;195m else\u001b[0m \u001b[0;30;48;2;166;216;84m:\u001b[0m \u001b[0;30;48;2;255;217;47m two\u001b[0m \u001b[0;30;48;2;102;194;165m tabs\u001b[0m \u001b[0;30;48;2;252;141;98m:\"\u001b[0m \u001b[0;30;48;2;141;160;203m \u001b[0m \u001b[0;30;48;2;231;138;195m \"\u001b[0m \u001b[0;30;48;2;166;216;84m Three\u001b[0m \u001b[0;30;48;2;255;217;47m tabs\u001b[0m \u001b[0;30;48;2;102;194;165m:\u001b[0m \u001b[0;30;48;2;252;141;98m \"\u001b[0m \u001b[0;30;48;2;141;160;203m \u001b[0m \u001b[0;30;48;2;231;138;195m \"\u001b[0m \u001b[0;30;48;2;166;216;84m\n",
"\u001b[0m \u001b[0;30;48;2;255;217;47m1\u001b[0m \u001b[0;30;48;2;102;194;165m2\u001b[0m \u001b[0;30;48;2;252;141;98m.\u001b[0m \u001b[0;30;48;2;141;160;203m0\u001b[0m \u001b[0;30;48;2;231;138;195m*\u001b[0m \u001b[0;30;48;2;166;216;84m5\u001b[0m \u001b[0;30;48;2;255;217;47m0\u001b[0m \u001b[0;30;48;2;102;194;165m=\u001b[0m \u001b[0;30;48;2;252;141;98m6\u001b[0m \u001b[0;30;48;2;141;160;203m0\u001b[0m \u001b[0;30;48;2;231;138;195m0\u001b[0m \u001b[0;30;48;2;166;216;84m\n",
"\u001b[0m "
]
}
],
"source": [
"# You need to request access before being able to use this tokenizer\n",
"show_tokens(text, \"bigcode/starcoder2-15b\")"
]
},
{
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"metadata": {
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"height": 220,
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]
},
"executionInfo": {
"elapsed": 1388,
"status": "ok",
"timestamp": 1719589605088,
"user": {
"displayName": "Jay Alammar جهاد العمار",
"userId": "14617748739431919458"
},
"user_tz": 240
},
"id": "KeWcUdxY6I3u",
"outputId": "f39c8f56-1e71-44bb-bade-75bfb33b581c"
},
"outputs": [
{
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"version_minor": 0
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"text/plain": [
"tokenizer_config.json: 0%| | 0.00/166 [00:00<?, ?B/s]"
]
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{
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{
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"\u001b[0;30;48;2;102;194;165m\n",
"\u001b[0m \u001b[0;30;48;2;252;141;98mEnglish\u001b[0m \u001b[0;30;48;2;141;160;203m and\u001b[0m \u001b[0;30;48;2;231;138;195m CAP\u001b[0m \u001b[0;30;48;2;166;216;84mITAL\u001b[0m \u001b[0;30;48;2;255;217;47mIZATION\u001b[0m \u001b[0;30;48;2;102;194;165m\n",
"\u001b[0m \u001b[0;30;48;2;252;141;98m<38>\u001b[0m \u001b[0;30;48;2;141;160;203m<33>\u001b[0m \u001b[0;30;48;2;231;138;195m<35>\u001b[0m \u001b[0;30;48;2;166;216;84m<34>\u001b[0m \u001b[0;30;48;2;255;217;47m <20>\u001b[0m \u001b[0;30;48;2;102;194;165m<35>\u001b[0m \u001b[0;30;48;2;252;141;98m<38>\u001b[0m \u001b[0;30;48;2;141;160;203m\n",
"\u001b[0m \u001b[0;30;48;2;231;138;195mshow\u001b[0m \u001b[0;30;48;2;166;216;84m_\u001b[0m \u001b[0;30;48;2;255;217;47mtokens\u001b[0m \u001b[0;30;48;2;102;194;165m False\u001b[0m \u001b[0;30;48;2;252;141;98m None\u001b[0m \u001b[0;30;48;2;141;160;203m elif\u001b[0m \u001b[0;30;48;2;231;138;195m \u001b[0m \u001b[0;30;48;2;166;216;84m==\u001b[0m \u001b[0;30;48;2;255;217;47m \u001b[0m \u001b[0;30;48;2;102;194;165m>\u001b[0m \u001b[0;30;48;2;252;141;98m=\u001b[0m \u001b[0;30;48;2;141;160;203m else\u001b[0m \u001b[0;30;48;2;231;138;195m:\u001b[0m \u001b[0;30;48;2;166;216;84m two\u001b[0m \u001b[0;30;48;2;255;217;47m t\u001b[0m \u001b[0;30;48;2;102;194;165mabs\u001b[0m \u001b[0;30;48;2;252;141;98m:\u001b[0m \u001b[0;30;48;2;141;160;203m\"\u001b[0m \u001b[0;30;48;2;231;138;195m \u001b[0m \u001b[0;30;48;2;166;216;84m\"\u001b[0m \u001b[0;30;48;2;255;217;47m Three\u001b[0m \u001b[0;30;48;2;102;194;165m t\u001b[0m \u001b[0;30;48;2;252;141;98mabs\u001b[0m \u001b[0;30;48;2;141;160;203m:\u001b[0m \u001b[0;30;48;2;231;138;195m \u001b[0m \u001b[0;30;48;2;166;216;84m\"\u001b[0m \u001b[0;30;48;2;255;217;47m \u001b[0m \u001b[0;30;48;2;102;194;165m\"\u001b[0m \u001b[0;30;48;2;252;141;98m\n",
"\u001b[0m \u001b[0;30;48;2;141;160;203m1\u001b[0m \u001b[0;30;48;2;231;138;195m2\u001b[0m \u001b[0;30;48;2;166;216;84m.\u001b[0m \u001b[0;30;48;2;255;217;47m0\u001b[0m \u001b[0;30;48;2;102;194;165m*\u001b[0m \u001b[0;30;48;2;252;141;98m5\u001b[0m \u001b[0;30;48;2;141;160;203m0\u001b[0m \u001b[0;30;48;2;231;138;195m=\u001b[0m \u001b[0;30;48;2;166;216;84m6\u001b[0m \u001b[0;30;48;2;255;217;47m0\u001b[0m \u001b[0;30;48;2;102;194;165m0\u001b[0m \u001b[0;30;48;2;252;141;98m\n",
"\u001b[0m "
]
}
],
"source": [
"show_tokens(text, \"facebook/galactica-1.3b\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 374,
"status": "ok",
"timestamp": 1719589632350,
"user": {
"displayName": "Jay Alammar جهاد العمار",
"userId": "14617748739431919458"
},
"user_tz": 240
},
"id": "__QNj2Cohzz2",
"outputId": "17ffab73-b07c-44a9-c482-64ab9f4c45a4"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\u001b[0m \u001b[0;30;48;2;231;138;195mEnglish\u001b[0m \u001b[0;30;48;2;166;216;84mand\u001b[0m \u001b[0;30;48;2;255;217;47mC\u001b[0m \u001b[0;30;48;2;102;194;165mAP\u001b[0m \u001b[0;30;48;2;252;141;98mIT\u001b[0m \u001b[0;30;48;2;141;160;203mAL\u001b[0m \u001b[0;30;48;2;231;138;195mIZ\u001b[0m \u001b[0;30;48;2;166;216;84mATION\u001b[0m \u001b[0;30;48;2;255;217;47m\n",
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"\u001b[0m \u001b[0;30;48;2;231;138;195mshow\u001b[0m \u001b[0;30;48;2;166;216;84m_\u001b[0m \u001b[0;30;48;2;255;217;47mto\u001b[0m \u001b[0;30;48;2;102;194;165mkens\u001b[0m \u001b[0;30;48;2;252;141;98mFalse\u001b[0m \u001b[0;30;48;2;141;160;203mNone\u001b[0m \u001b[0;30;48;2;231;138;195melif\u001b[0m \u001b[0;30;48;2;166;216;84m==\u001b[0m \u001b[0;30;48;2;255;217;47m>=\u001b[0m \u001b[0;30;48;2;102;194;165melse\u001b[0m \u001b[0;30;48;2;252;141;98m:\u001b[0m \u001b[0;30;48;2;141;160;203mtwo\u001b[0m \u001b[0;30;48;2;231;138;195mtabs\u001b[0m \u001b[0;30;48;2;166;216;84m:\"\u001b[0m \u001b[0;30;48;2;255;217;47m \u001b[0m \u001b[0;30;48;2;102;194;165m\"\u001b[0m \u001b[0;30;48;2;252;141;98mThree\u001b[0m \u001b[0;30;48;2;141;160;203mtabs\u001b[0m \u001b[0;30;48;2;231;138;195m:\u001b[0m \u001b[0;30;48;2;166;216;84m\"\u001b[0m \u001b[0;30;48;2;255;217;47m \u001b[0m \u001b[0;30;48;2;102;194;165m\"\u001b[0m \u001b[0;30;48;2;252;141;98m\n",
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"\u001b[0m "
]
}
],
"source": [
"show_tokens(text, \"microsoft/Phi-3-mini-4k-instruct\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9Tu7OY4HvBEm"
},
"source": [
"# Contextualized Word Embeddings From a Language Model (Like BERT)"
]
},
{
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"metadata": {
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},
"executionInfo": {
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"status": "ok",
"timestamp": 1719641476949,
"user": {
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"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "nsjz-VsYu9bB",
"outputId": "03ea124b-c6de-449d-ea6f-f5e5b84c2c97"
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{
"data": {
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"model_id": "761c3e6c7f26453bba2f463f39f3ae73",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/52.0 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n"
]
},
{
"data": {
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"model_id": "8ee98d9d017542609881a1e16a8f393f",
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"version_minor": 0
},
"text/plain": [
"config.json: 0%| | 0.00/474 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"vocab.json: 0%| | 0.00/899k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"version_minor": 0
},
"text/plain": [
"merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"version_major": 2,
"version_minor": 0
},
"text/plain": [
"config.json: 0%| | 0.00/578 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7570614d79184ab2b44700df2342b294",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"pytorch_model.bin: 0%| | 0.00/241M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from transformers import AutoModel, AutoTokenizer\n",
"\n",
"# Load a tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"microsoft/deberta-base\")\n",
"\n",
"# Load a language model\n",
"model = AutoModel.from_pretrained(\"microsoft/deberta-v3-xsmall\")\n",
"\n",
"# Tokenize the sentence\n",
"tokens = tokenizer('Hello world', return_tensors='pt')\n",
"\n",
"# Process the tokens\n",
"output = model(**tokens)[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 567,
"status": "ok",
"timestamp": 1719641482036,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "lQly_KcbvDce",
"outputId": "fe2cc467-2a5a-4111-8d23-4da9aa799b79"
},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 4, 384])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 2,
"status": "ok",
"timestamp": 1719641482353,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "8GcRrpPV0kVj",
"outputId": "93766ff1-1ae5-4e90-dba0-286d9e721c3d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[CLS]\n",
"Hello\n",
" world\n",
"[SEP]\n"
]
}
],
"source": [
"for token in tokens['input_ids'][0]:\n",
" print(tokenizer.decode(token))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 1,
"status": "ok",
"timestamp": 1719641482353,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "e8oHVC7B0lkk",
"outputId": "f7dd1e0c-a2db-4ae4-8ccb-c97fa150071a"
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[-3.4816, 0.0861, -0.1819, ..., -0.0612, -0.3911, 0.3017],\n",
" [ 0.1898, 0.3208, -0.2315, ..., 0.3714, 0.2478, 0.8048],\n",
" [ 0.2071, 0.5036, -0.0485, ..., 1.2175, -0.2292, 0.8582],\n",
" [-3.4278, 0.0645, -0.1427, ..., 0.0658, -0.4367, 0.3834]]],\n",
" grad_fn=<NativeLayerNormBackward0>)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DdEDuLWa0r4L"
},
"source": [
"# Text Embeddings (For Sentences and Whole Documents)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425,
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"executionInfo": {
"elapsed": 7006,
"status": "ok",
"timestamp": 1719641491724,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "TQHWioIc0pQ8",
"outputId": "87112ec7-bee0-4894-d850-8dd5e0f4e38c"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a50156f59d8548b683982af06d5bda09",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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},
"text/plain": [
"config_sentence_transformers.json: 0%| | 0.00/116 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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},
"text/plain": [
"README.md: 0%| | 0.00/10.6k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"sentence_bert_config.json: 0%| | 0.00/53.0 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
" warnings.warn(\n"
]
},
{
"data": {
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"model_id": "784748de51254ba18128af8df30b8a93",
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"config.json: 0%| | 0.00/571 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"model_id": "f0793746dff34da8858758fb55284b97",
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"version_minor": 0
},
"text/plain": [
"model.safetensors: 0%| | 0.00/438M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ca7eb54b296c4a1fa91678c0e3d65f5d",
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},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/363 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2895fad80e754b4e8158c6dd8db69058",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "652ddbc085994a36b553ea04359943a1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer.json: 0%| | 0.00/466k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4688efe8ab954510b30df18f8daa74a5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/239 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1f5849878621437397efe2de7b7a43fe",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"1_Pooling/config.json: 0%| | 0.00/190 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sentence_transformers import SentenceTransformer\n",
"\n",
"# Load model\n",
"model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\n",
"\n",
"# Convert text to text embeddings\n",
"vector = model.encode(\"Best movie ever!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 2,
"status": "ok",
"timestamp": 1719641491724,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "PDwfmBiC0uER",
"outputId": "db6755ce-92b2-45d1-85aa-9b53baee446e"
},
"outputs": [
{
"data": {
"text/plain": [
"(768,)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vector.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xnuGRjo80yKj"
},
"source": [
"# Word Embeddings Beyond LLMs\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 44634,
"status": "ok",
"timestamp": 1719641543423,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "sKgNdnwe0vfK",
"outputId": "180bbb09-b030-4fa0-9198-085b0eb54c7b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[==================================================] 100.0% 66.0/66.0MB downloaded\n"
]
}
],
"source": [
"import gensim.downloader as api\n",
"\n",
"# Download embeddings (66MB, glove, trained on wikipedia, vector size: 50)\n",
"# Other options include \"word2vec-google-news-300\"\n",
"# More options at https://github.com/RaRe-Technologies/gensim-data\n",
"model = api.load(\"glove-wiki-gigaword-50\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 2,
"status": "ok",
"timestamp": 1719641543423,
"user": {
"displayName": "Maarten Grootendorst",
"userId": "11015108362723620659"
},
"user_tz": -120
},
"id": "u_vj5NVn01aD",
"outputId": "73c3edd8-0185-494d-a842-d78cbe100642"
},
"outputs": [
{
"data": {
"text/plain": [
"[('king', 1.0000001192092896),\n",
" ('prince', 0.8236179351806641),\n",
" ('queen', 0.7839043140411377),\n",
" ('ii', 0.7746230363845825),\n",
" ('emperor', 0.7736247777938843),\n",
" ('son', 0.766719400882721),\n",
" ('uncle', 0.7627150416374207),\n",
" ('kingdom', 0.7542161345481873),\n",
" ('throne', 0.7539914846420288),\n",
" ('brother', 0.7492411136627197),\n",
" ('ruler', 0.7434253692626953)]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.most_similar([model['king']], topn=11)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QMSgyKKS4xUx"
},
"source": [
"# Recommending songs by embeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3dJdWzT67nDL"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from urllib import request\n",
"\n",
"# Get the playlist dataset file\n",
"data = request.urlopen('https://storage.googleapis.com/maps-premium/dataset/yes_complete/train.txt')\n",
"\n",
"# Parse the playlist dataset file. Skip the first two lines as\n",
"# they only contain metadata\n",
"lines = data.read().decode(\"utf-8\").split('\\n')[2:]\n",
"\n",
"# Remove playlists with only one song\n",
"playlists = [s.rstrip().split() for s in lines if len(s.split()) > 1]\n",
"\n",
"# Load song metadata\n",
"songs_file = request.urlopen('https://storage.googleapis.com/maps-premium/dataset/yes_complete/song_hash.txt')\n",
"songs_file = songs_file.read().decode(\"utf-8\").split('\\n')\n",
"songs = [s.rstrip().split('\\t') for s in songs_file]\n",
"songs_df = pd.DataFrame(data=songs, columns = ['id', 'title', 'artist'])\n",
"songs_df = songs_df.set_index('id')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1724598630488,
"user": {
"displayName": "Jay Alammar جهاد العمار",
"userId": "14617748739431919458"
},
"user_tz": 240
},
"id": "Q3zirG-lo3H8",
"outputId": "e3b4269e-dd42-428e-8b28-46c27d0231af"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Playlist #1:\n",
" ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '2', '42', '43', '44', '45', '46', '47', '48', '20', '49', '8', '50', '51', '52', '53', '54', '55', '56', '57', '25', '58', '59', '60', '61', '62', '3', '63', '64', '65', '66', '46', '47', '67', '2', '48', '68', '69', '70', '57', '50', '71', '72', '53', '73', '25', '74', '59', '20', '46', '75', '76', '77', '59', '20', '43'] \n",
"\n",
"Playlist #2:\n",
" ['78', '79', '80', '3', '62', '81', '14', '82', '48', '83', '84', '17', '85', '86', '87', '88', '74', '89', '90', '91', '4', '73', '62', '92', '17', '53', '59', '93', '94', '51', '50', '27', '95', '48', '96', '97', '98', '99', '100', '57', '101', '102', '25', '103', '3', '104', '105', '106', '107', '47', '108', '109', '110', '111', '112', '113', '25', '63', '62', '114', '115', '84', '116', '117', '118', '119', '120', '121', '122', '123', '50', '70', '71', '124', '17', '85', '14', '82', '48', '125', '47', '46', '72', '53', '25', '73', '4', '126', '59', '74', '20', '43', '127', '128', '129', '13', '82', '48', '130', '131', '132', '133', '134', '135', '136', '137', '59', '46', '138', '43', '20', '139', '140', '73', '57', '70', '141', '3', '1', '74', '142', '143', '144', '145', '48', '13', '25', '146', '50', '147', '126', '59', '20', '148', '149', '150', '151', '152', '56', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '60', '176', '51', '177', '178', '179', '180', '181', '182', '183', '184', '185', '57', '186', '187', '188', '189', '190', '191', '46', '192', '193', '194', '195', '196', '197', '198', '25', '199', '200', '49', '201', '100', '202', '203', '204', '205', '206', '207', '32', '208', '209', '210']\n"
]
}
],
"source": [
"print( 'Playlist #1:\\n ', playlists[0], '\\n')\n",
"print( 'Playlist #2:\\n ', playlists[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EaUz3E0P7sJs"
},
"outputs": [],
"source": [
"from gensim.models import Word2Vec\n",
"\n",
"# Train our Word2Vec model\n",
"model = Word2Vec(\n",
" playlists, vector_size=32, window=20, negative=50, min_count=1, workers=4\n",
")"
]
},
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"id": "9EFGWesO8rOJ",
"outputId": "1e46ce56-7b14-4268-a38a-c328e0f52943"
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"outputs": [
{
"data": {
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"execution_count": 23,
"metadata": {},
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],
"source": [
"song_id = 2172\n",
"\n",
"# Ask the model for songs similar to song #2172\n",
"model.wv.most_similar(positive=str(song_id))"
]
},
{
"cell_type": "code",
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"metadata": {
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"base_uri": "https://localhost:8080/"
},
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"id": "AMiY6isXqKk4",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"title Fade To Black\n",
"artist Metallica\n",
"Name: 2172 , dtype: object\n"
]
}
],
"source": [
"print(songs_df.iloc[2172])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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" title artist\n",
"id \n",
"2849 Run To The Hills Iron Maiden\n",
"2640 Red Barchetta Rush\n",
"3167 Unchained Van Halen\n",
"5549 November Rain Guns N' Roses\n",
"2715 Rainbow In The Dark Dio"
]
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],
"source": [
"import numpy as np\n",
"\n",
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" similar_songs = np.array(\n",
" model.wv.most_similar(positive=str(song_id),topn=5)\n",
" )[:,0]\n",
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"title Fade To Black\n",
"artist Metallica\n",
"Name: 2172 , dtype: object\n",
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"2849 Run To The Hills Iron Maiden\n",
"2640 Red Barchetta Rush\n",
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"5549 November Rain Guns N' Roses\n",
"2715 Rainbow In The Dark Dio"
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"title California Love (w\\/ Dr. Dre & Roger Troutman)\n",
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" title artist\n",
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"5668 How We Do (w\\/ 50 Cent) The Game\n",
"413 If I Ruled The World (Imagine That) (w\\/ Laury... Nas\n",
"5661 Sweet Dreams Beyonce\n",
"330 Hate It Or Love It (w\\/ 50 Cent) The Game\n",
"886 Heartless Kanye West"
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