576 lines
18 KiB
Plaintext
576 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NXTSugt6ieXh"
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},
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"source": [
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"## Melatih Model CBoW\n",
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"\n",
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"Notebook ini adalah bagian dari [Kurikulum AI untuk Pemula](http://aka.ms/ai-beginners)\n",
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"\n",
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"Dalam contoh ini, kita akan mempelajari cara melatih model bahasa CBoW untuk mendapatkan ruang embedding Word2Vec kita sendiri. Kita akan menggunakan dataset AG News sebagai sumber teks.\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import torch\n",
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"import torchtext\n",
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"import os\n",
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"import collections\n",
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"import builtins\n",
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"import random\n",
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"import numpy as np"
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],
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"metadata": {
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"id": "q-UiiJUKaxHj"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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],
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"metadata": {
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"id": "TFbR8CZaTZ1q"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Pertama, mari kita muat dataset kita dan definisikan tokenizer serta kosakata. Kita akan menetapkan `vocab_size` ke 5000 untuk membatasi perhitungan sedikit.\n"
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],
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"metadata": {
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"id": "HIwC7lI5T-ov"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"def load_dataset(ngrams = 1, min_freq = 1, vocab_size = 5000 , lines_cnt = 500):\n",
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" tokenizer = torchtext.data.utils.get_tokenizer('basic_english')\n",
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" print(\"Loading dataset...\")\n",
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" test_dataset, train_dataset = torchtext.datasets.AG_NEWS(root='./data')\n",
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" train_dataset = list(train_dataset)\n",
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" test_dataset = list(test_dataset)\n",
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" classes = ['World', 'Sports', 'Business', 'Sci/Tech']\n",
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" print('Building vocab...')\n",
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" counter = collections.Counter()\n",
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" for i, (_, line) in enumerate(train_dataset):\n",
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" counter.update(torchtext.data.utils.ngrams_iterator(tokenizer(line),ngrams=ngrams))\n",
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" if i == lines_cnt:\n",
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" break\n",
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" vocab = torchtext.vocab.Vocab(collections.Counter(dict(counter.most_common(vocab_size))), min_freq=min_freq)\n",
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" return train_dataset, test_dataset, classes, vocab, tokenizer"
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],
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"metadata": {
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"id": "wdZuygtgiuLG"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"train_dataset, test_dataset, _, vocab, tokenizer = load_dataset()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "4d1nU1gsivGu",
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"outputId": "949fe272-ae0e-49f5-c373-6703458b3a74"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Loading dataset...\n",
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"Building vocab...\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"def encode(x, vocabulary, tokenizer = tokenizer):\n",
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" return [vocabulary[s] for s in tokenizer(x)]"
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],
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"metadata": {
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"id": "1XDYNhG8ToFV"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "LIlQk6_PaHVY"
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},
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"source": [
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"## Model CBoW\n",
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"\n",
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"CBoW mempelajari cara memprediksi sebuah kata berdasarkan $2N$ kata di sekitarnya. Sebagai contoh, ketika $N=1$, kita akan mendapatkan pasangan berikut dari kalimat *I like to train networks*: (like, I), (I, like), (to, like), (like, to), (train, to), (to, train), (networks, train), (train, networks). Di sini, kata pertama adalah kata di sekitar yang digunakan sebagai input, dan kata kedua adalah kata yang kita prediksi.\n",
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"\n",
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"Untuk membangun jaringan yang dapat memprediksi kata berikutnya, kita perlu memberikan kata di sekitar sebagai input, dan mendapatkan nomor kata sebagai output. Arsitektur jaringan CBoW adalah sebagai berikut:\n",
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"\n",
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"* Kata input dilewatkan melalui lapisan embedding. Lapisan embedding ini akan menjadi embedding Word2Vec kita, sehingga kita akan mendefinisikannya secara terpisah sebagai variabel `embedder`. Dalam contoh ini, kita akan menggunakan ukuran embedding = 30, meskipun Anda mungkin ingin bereksperimen dengan dimensi yang lebih tinggi (Word2Vec yang sebenarnya memiliki 300).\n",
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"* Vektor embedding kemudian akan dilewatkan ke lapisan linear yang akan memprediksi kata output. Oleh karena itu, lapisan ini memiliki neuron sebanyak `vocab_size`.\n",
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"\n",
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"Untuk outputnya, jika kita menggunakan `CrossEntropyLoss` sebagai fungsi loss, kita juga hanya perlu memberikan nomor kata sebagai hasil yang diharapkan, tanpa menggunakan one-hot encoding.\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"vocab_size = len(vocab)\n",
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"\n",
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"embedder = torch.nn.Embedding(num_embeddings = vocab_size, embedding_dim = 30)\n",
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"model = torch.nn.Sequential(\n",
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" embedder,\n",
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" torch.nn.Linear(in_features = 30, out_features = vocab_size),\n",
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")\n",
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"\n",
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"print(model)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "akKTcKQKkfl2",
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"outputId": "da687e3e-a8ec-4c1a-e456-ab8cd6ac7dad"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Sequential(\n",
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" (0): Embedding(5002, 30)\n",
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" (1): Linear(in_features=30, out_features=5002, bias=True)\n",
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")\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Nud6jgGPaHVa"
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},
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"source": [
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"## Mempersiapkan Data Pelatihan\n",
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"\n",
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"Sekarang mari kita buat fungsi utama yang akan menghitung pasangan kata CBoW dari teks. Fungsi ini akan memungkinkan kita menentukan ukuran jendela, dan akan mengembalikan satu set pasangan - kata input dan output. Perlu dicatat bahwa fungsi ini dapat digunakan pada kata, serta pada vektor/tensor - yang akan memungkinkan kita untuk mengenkripsi teks sebelum meneruskannya ke fungsi `to_cbow`.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "x-dsXygOieXn",
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"outputId": "c2218280-e540-40ba-9546-efe48d0d714f"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"[['like', 'I'], ['to', 'I'], ['I', 'like'], ['to', 'like'], ['train', 'like'], ['I', 'to'], ['like', 'to'], ['train', 'to'], ['networks', 'to'], ['like', 'train'], ['to', 'train'], ['networks', 'train'], ['to', 'networks'], ['train', 'networks']]\n",
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"[[232, 172], [5, 172], [172, 232], [5, 232], [0, 232], [172, 5], [232, 5], [0, 5], [1202, 5], [232, 0], [5, 0], [1202, 0], [5, 1202], [0, 1202]]\n"
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]
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}
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],
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"source": [
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"def to_cbow(sent,window_size=2):\n",
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" res = []\n",
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" for i,x in enumerate(sent):\n",
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" for j in range(max(0,i-window_size),min(i+window_size+1,len(sent))):\n",
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" if i!=j:\n",
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" res.append([sent[j],x])\n",
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" return res\n",
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"\n",
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"print(to_cbow(['I','like','to','train','networks']))\n",
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"print(to_cbow(encode('I like to train networks', vocab)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "XVaaDLjaaHVb"
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},
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"source": [
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"Mari siapkan dataset pelatihan. Kita akan melalui semua berita, memanggil `to_cbow` untuk mendapatkan daftar pasangan kata, dan menambahkan pasangan tersebut ke `X` dan `Y`. Demi menghemat waktu, kita hanya akan mempertimbangkan 10 ribu berita pertama - Anda dapat dengan mudah menghapus batasan ini jika memiliki lebih banyak waktu untuk menunggu, dan ingin mendapatkan embedding yang lebih baik :)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "54b-Gd9TieXo"
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},
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"outputs": [],
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"source": [
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"X = []\n",
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"Y = []\n",
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"for i, x in zip(range(10000), train_dataset):\n",
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" for w1, w2 in to_cbow(encode(x[1], vocab), window_size = 5):\n",
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" X.append(w1)\n",
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" Y.append(w2)\n",
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"\n",
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"X = torch.tensor(X)\n",
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"Y = torch.tensor(Y)"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Kami juga akan mengubah data tersebut menjadi satu dataset, dan membuat dataloader:\n"
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],
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"metadata": {
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"id": "cwWy0PzXWhN5"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"class SimpleIterableDataset(torch.utils.data.IterableDataset):\n",
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" def __init__(self, X, Y):\n",
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" super(SimpleIterableDataset).__init__()\n",
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" self.data = []\n",
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" for i in range(len(X)):\n",
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" self.data.append( (Y[i], X[i]) )\n",
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" random.shuffle(self.data)\n",
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"\n",
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" def __iter__(self):\n",
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" return iter(self.data)"
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],
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"metadata": {
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"id": "mfoAcGPFZU8p"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "e4NQ_-5waHVc"
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},
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"source": [
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"Kami juga akan mengubah data tersebut menjadi satu dataset, dan membuat dataloader:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "AbLUcojlieXo"
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},
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"outputs": [],
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"source": [
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"ds = SimpleIterableDataset(X, Y)\n",
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"dl = torch.utils.data.DataLoader(ds, batch_size = 256)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "pKQr7sXeaHVc"
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},
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"source": [
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"Sekarang mari kita lakukan pelatihan sebenarnya. Kita akan menggunakan optimizer `SGD` dengan tingkat pembelajaran yang cukup tinggi. Anda juga dapat mencoba menggunakan optimizer lain, seperti `Adam`. Kita akan melatih selama 10 epoch untuk memulai - dan Anda dapat menjalankan ulang sel ini jika Anda menginginkan loss yang lebih rendah.\n"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"def train_epoch(net, dataloader, lr = 0.01, optimizer = None, loss_fn = torch.nn.CrossEntropyLoss(), epochs = None, report_freq = 1):\n",
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" optimizer = optimizer or torch.optim.Adam(net.parameters(), lr = lr)\n",
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" loss_fn = loss_fn.to(device)\n",
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" net.train()\n",
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"\n",
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" for i in range(epochs):\n",
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" total_loss, j = 0, 0, \n",
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" for labels, features in dataloader:\n",
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" optimizer.zero_grad()\n",
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" features, labels = features.to(device), labels.to(device)\n",
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" out = net(features)\n",
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" loss = loss_fn(out, labels)\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" total_loss += loss\n",
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" j += 1\n",
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" if i % report_freq == 0:\n",
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" print(f\"Epoch: {i+1}: loss={total_loss.item()/j}\")\n",
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"\n",
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" return total_loss.item()/j"
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],
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"metadata": {
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"id": "HeeCYKr_KF1w"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"train_epoch(net = model, dataloader = dl, optimizer = torch.optim.SGD(model.parameters(), lr = 0.1), loss_fn = torch.nn.CrossEntropyLoss(), epochs = 10)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "KVgwGtDHgDlT",
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"outputId": "2447833f-f0e3-4566-c33d-addbfe2f451d"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Epoch: 1: loss=5.664632366860172\n",
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"Epoch: 2: loss=5.632101973960962\n",
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"Epoch: 3: loss=5.610399051405015\n",
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"Epoch: 4: loss=5.594621561080262\n",
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"Epoch: 5: loss=5.582538017415446\n",
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"Epoch: 6: loss=5.572900234519603\n",
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"Epoch: 7: loss=5.564951676341915\n",
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"Epoch: 8: loss=5.558288112064614\n",
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"Epoch: 9: loss=5.552576955031129\n",
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"Epoch: 10: loss=5.547634165194347\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"5.547634165194347"
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]
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},
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"metadata": {},
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"execution_count": 16
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "W8u2qXZmaHVd"
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},
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"source": [
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"## Mencoba Word2Vec\n",
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"\n",
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"Untuk menggunakan Word2Vec, mari kita ekstrak vektor yang sesuai dengan semua kata dalam kosakata kita:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "r8TatcXjkU_t"
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},
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"outputs": [],
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"source": [
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"vectors = torch.stack([embedder(torch.tensor(vocab[s])) for s in vocab.itos], 0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "3OcX21UOaHVd"
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},
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"source": [
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"Mari kita lihat, misalnya, bagaimana kata **Paris** dikodekan menjadi sebuah vektor:\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "bz6tAeLzieXp",
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"outputId": "5b20850e-4342-45e9-f840-cfac2b4d61d8"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"tensor([-0.0915, 2.1224, -0.0281, -0.6819, 1.1219, 0.6458, -1.3704, -1.3314,\n",
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" -1.1437, 0.4496, 0.2301, -0.3515, -0.8485, 1.0481, 0.4386, -0.8949,\n",
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" 0.5644, 1.0939, -2.5096, 3.2949, -0.2601, -0.8640, 0.1421, -0.0804,\n",
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" -0.5083, -1.0560, 0.9753, -0.5949, -1.6046, 0.5774],\n",
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" grad_fn=<EmbeddingBackward>)\n"
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]
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}
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],
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"source": [
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"paris_vec = embedder(torch.tensor(vocab['paris']))\n",
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"print(paris_vec)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "pHTJlaeYaHVd"
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},
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"source": [
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"Sangat menarik menggunakan Word2Vec untuk mencari sinonim. Fungsi berikut akan mengembalikan `n` kata terdekat dengan input yang diberikan. Untuk menemukannya, kita menghitung norma $|w_i - v|$, di mana $v$ adalah vektor yang sesuai dengan kata input kita, dan $w_i$ adalah encoding dari kata ke-`i` dalam kosakata. Kemudian kita mengurutkan array dan mengembalikan indeks yang sesuai menggunakan `argsort`, serta mengambil `n` elemen pertama dari daftar, yang mengkodekan posisi kata terdekat dalam kosakata.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "NlZyi-_olFar",
|
|
"outputId": "b5dbb163-88c4-4d5a-eaf2-6751f700e98c"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"['microsoft', 'quoted', 'lp', 'rate', 'top']"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 56
|
|
}
|
|
],
|
|
"source": [
|
|
"def close_words(x, n = 5):\n",
|
|
" vec = embedder(torch.tensor(vocab[x]))\n",
|
|
" top5 = np.linalg.norm(vectors.detach().numpy() - vec.detach().numpy(), axis = 1).argsort()[:n]\n",
|
|
" return [ vocab.itos[x] for x in top5 ]\n",
|
|
"\n",
|
|
"close_words('microsoft')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "-dQq7xeAln0U",
|
|
"outputId": "66f768c3-c248-4bfd-ce4f-c8ffc6d0dd0d"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"['basketball', 'lot', 'sinai', 'states', 'healthdaynews']"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 51
|
|
}
|
|
],
|
|
"source": [
|
|
"close_words('basketball')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "fJXqK26b29sa",
|
|
"outputId": "78f0baba-ffd0-485a-dd87-0a12bedfd7fa"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"['funds', 'travel', 'sydney', 'japan', 'business']"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 77
|
|
}
|
|
],
|
|
"source": [
|
|
"close_words('funds')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "My0VeTDd3Ji8"
|
|
},
|
|
"source": [
|
|
"## Intisari\n",
|
|
"\n",
|
|
"Dengan menggunakan teknik-teknik cerdas seperti CBoW, kita dapat melatih model Word2Vec. Anda juga bisa mencoba melatih model skip-gram yang dilatih untuk memprediksi kata di sekitarnya berdasarkan kata pusat, dan melihat seberapa baik kinerjanya.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n---\n\n**Penafian**: \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berusaha untuk memberikan hasil yang akurat, harap diketahui bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang otoritatif. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan profesional oleh manusia. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"name": "CBoW-PyTorch.ipynb",
|
|
"provenance": []
|
|
},
|
|
"interpreter": {
|
|
"hash": "16af2a8bbb083ea23e5e41c7f5787656b2ce26968575d8763f2c4b17f9cd711f"
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.12 ('py38')",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.12"
|
|
},
|
|
"orig_nbformat": 4,
|
|
"gpuClass": "standard",
|
|
"coopTranslator": {
|
|
"original_hash": "36df28efe3fe40b6fb0a7fa48fe3ea82",
|
|
"translation_date": "2025-08-29T15:48:46+00:00",
|
|
"source_file": "lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb",
|
|
"language_code": "id"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
} |