{
"cells": [
{
"cell_type": "markdown",
"id": "b39e9d1f-05b5-43b4-b50a-036ae88657cd",
"metadata": {},
"source": [
"# Next-Token-Prediction\n",
"This is based on the following blog posts: \n",
"* Predicting Next Word — NLP & Deep Learning: https://medium.com/@vijay2340025/predicting-next-word-nlp-deep-learning-85010d966671"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9c35813f-ccb3-42b3-a7e1-6069dece172f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import nltk\n",
"import pandas as pd\n",
"import torch\n",
"import numpy as np\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b482fac0-b576-472e-ab5c-df951d0b2404",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /home/container/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.download('punkt')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f6a376c4-bd61-44d0-badc-8c451394b627",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"dataset = \"\"\"\n",
" Is Antwerp a city?,\n",
" Is Antwerp a municipality?,\n",
" Is Antwerp in Belgium?,\n",
" What is Antwerp?,\n",
" What is the population of the city of Antwerp?,\n",
" Where is the city of Antwerp?,\n",
" Why is Antwerp important to fashion?,\n",
" Antwerp is to the east of what river?,\n",
" How many municipalities does Antwerp have?,\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c5831581-84e1-41d1-ae4f-b87e5bab365c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def get_all_possible_sequences(text):\n",
" seq = []\n",
" words = nltk.word_tokenize(text)\n",
" total_words = len(words)\n",
" for i in range(1, total_words):\n",
" for j in range(1, len(words)-i+1):\n",
" arr = words[j-1:j+i]\n",
" seq.append((arr[:-1], arr[-1]))\n",
" return seq\n",
"def build_vocabulary(docs):\n",
" vocabulary = []\n",
" for doc in docs:\n",
" for w in nltk.word_tokenize(doc):\n",
" if w not in vocabulary:\n",
" vocabulary.append(w)\n",
" vocabulary.append('UNK')\n",
" return vocabulary"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2171ca5a-ca9a-4e33-86d0-8fa4b612f8a0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"docs = []\n",
"for row in dataset.split(\",\"):\n",
" docs.append(row.lower())\n",
"\n",
"lst = []\n",
"for doc in docs:\n",
" tmp_lst = get_all_possible_sequences(doc)\n",
" lst = lst + tmp_lst\n",
"\n",
"vocabulary = build_vocabulary(docs)\n",
"id2word = {idx: w for (idx, w) in enumerate(vocabulary)}\n",
"word2id = {w: idx for (idx, w) in enumerate(vocabulary)}\n",
"def seq2id(arr):\n",
" return torch.tensor([word2id[i] for i in arr])\n",
"def get_max_seq():\n",
" return len(list(set([len(i[0]) for i in lst])))\n",
"MAX_SEQ_LEN = get_max_seq()\n",
"def get_padded_x(data):\n",
" new_data = F.pad(input=data.view(1,-1), pad=(0, MAX_SEQ_LEN-data.shape[0], 0, 0), mode='constant', value=word2id['UNK'])\n",
" return new_data\n",
"def get_xy_vector(arr):\n",
" x = seq2id(arr[0])\n",
" y = seq2id([arr[1]])\n",
" return x, y"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bb8929f7-85be-4740-be07-7dd6b4ed3086",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"class NextWordModel(nn.Module):\n",
" \"\"\" Prediction of Next word based on the MAX_SEQ_LEN Sequence \"\"\"\n",
" def __init__(self, embedding_dim, hidden_dim, vocab_size):\n",
" super(NextWordModel, self).__init__()\n",
" self.hidden_dim = hidden_dim\n",
" self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)\n",
" self.gru = nn.GRU(embedding_dim * MAX_SEQ_LEN, hidden_dim)\n",
" self.linear = nn.Linear(hidden_dim, vocab_size)\n",
"\n",
" def forward(self, sentence):\n",
" embeds = self.word_embeddings(sentence)\n",
" lstm_out, _ = self.gru(embeds.view(1, 1, -1))\n",
" x = self.linear(lstm_out.view(1, -1))\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "08b6f539-75a0-4a18-b462-165ca44387d3",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on cuda:0\n"
]
}
],
"source": [
"if torch.cuda.is_available():\n",
" dev = \"cuda:0\"\n",
"else:\n",
" dev = \"cpu\"\n",
"print(f'Running on {dev}')\n",
"# set the model to be copied on GPU\n",
"device = torch.device(dev)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cf4f67b2-e0a4-4b57-abd9-4a2e40898511",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished\n"
]
}
],
"source": [
"EMBEDDING_DIM = 10\n",
"NO_OF_EPOCHS = 300\n",
"HIDDEN_DIM = len(vocabulary)\n",
"model = NextWordModel(EMBEDDING_DIM, HIDDEN_DIM, len(vocabulary))\n",
"loss_function = nn.CrossEntropyLoss()\n",
"optimizer = optim.SGD(model.parameters(), lr=0.1)\n",
"model.to(device)\n",
"for epoch in range(NO_OF_EPOCHS):\n",
" running_loss = 0.0\n",
" i = 0\n",
" for data in lst:\n",
" model.zero_grad()\n",
" x, y = get_xy_vector(data)\n",
"# convert to max seq length with padding\n",
" x = get_padded_x(x)\n",
"\n",
" x = x.to(device)\n",
" y = y.to(device)\n",
"\n",
" predicted = model(x)\n",
"\n",
" loss = loss_function(predicted, y)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" running_loss += loss\n",
" i += 1\n",
" if i % 100 == 0:\n",
" #print(f'Loss at iteration {i} and epoch {epoch} is {running_loss / 100}')\n",
" running_loss = 0\n",
"print('Finished')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "84e84dfc-5b7a-47c2-96cd-304c5630a002",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Type something here . . .\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
" what\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Answer :what is \n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
" what is\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Answer :what is the \n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
" what is the\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Answer :what is the population \n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
" what is the population\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Answer :what is the population of \n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
" what is the population of\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Answer :what is the population of the \n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
" what is the population of the\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Answer :what is the population of the city \n"
]
}
],
"source": [
"with torch.no_grad():\n",
" print('Type something here . . .')\n",
" while True:\n",
" inp = input(\"\")\n",
" inp = inp.strip()\n",
" if inp == \"q\":\n",
" break\n",
"\n",
" tokens = nltk.word_tokenize(inp.lower())\n",
" x = seq2id(tokens)\n",
" x = get_padded_x(x)\n",
"\n",
" x = x.to(device)\n",
" predicted = model(x).to(device)\n",
"\n",
" predicted = predicted[0].cpu().numpy()\n",
"\n",
" print(f'Answer: {inp} {id2word[np.argmax(predicted)]} ')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "660e6502-7548-47df-a645-a1e2d33178f6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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