{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "dfd117bd-5d6f-46e6-979c-092a8065fa0b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"S:\\WENV_TORCHTEXT\\Lib\\site-packages\\torchtext\\vocab\\__init__.py:4: UserWarning: \n",
"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n",
"S:\\WENV_TORCHTEXT\\Lib\\site-packages\\torchtext\\utils.py:4: UserWarning: \n",
"/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n",
"Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n",
" warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"
]
}
],
"source": [
"from torch.utils.data import IterableDataset, DataLoader\n",
"from torchtext.vocab import build_vocab_from_iterator\n",
"\n",
"import regex as re\n",
"import sys\n",
"import itertools\n",
"from itertools import islice\n",
"\n",
"from torch import nn\n",
"import torch\n",
"\n",
"from tqdm.notebook import tqdm\n",
"\n",
"embed_size = 300\n",
"vocab_size = 30_000\n",
"num_epochs = 1\n",
"device = 'cuda'\n",
"batch_size = 8192\n",
"train_file_path = 'train/train.txt'\n",
"\n",
"with open(train_file_path, 'r', encoding='utf-8') as file:\n",
" total = len(file.readlines())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "40392665-79bc-4032-a5de-9d189545c9f7",
"metadata": {},
"outputs": [],
"source": [
"# Function to extract words from a line of text\n",
"def get_words_from_line(line):\n",
" line = line.rstrip()\n",
" yield ''\n",
" for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
" yield m.group(0).lower()\n",
" yield ''\n",
"\n",
"# Generator to read lines from a file\n",
"def get_word_lines_from_file(file_name):\n",
" limit = total * 2\n",
" with open(file_name, 'r', encoding='utf8') as fh:\n",
" for line in fh:\n",
" limit -= 1\n",
" if not limit:\n",
" break\n",
" yield get_words_from_line(line)\n",
"\n",
"# Function to create trigrams from a sequence\n",
"def look_ahead_iterator(gen):\n",
" prev1, prev2 = None, None\n",
" for item in gen:\n",
" if prev1 is not None and prev2 is not None:\n",
" yield (prev2, prev1, item)\n",
" prev2 = prev1\n",
" prev1 = item\n",
"\n",
"# Dataset class for trigrams\n",
"class Trigrams(IterableDataset):\n",
" def __init__(self, text_file, vocabulary_size):\n",
" self.vocab = build_vocab_from_iterator(\n",
" get_word_lines_from_file(text_file),\n",
" max_tokens=vocabulary_size,\n",
" specials=['']\n",
" )\n",
" self.vocab.set_default_index(self.vocab[''])\n",
" self.vocabulary_size = vocabulary_size\n",
" self.text_file = text_file\n",
"\n",
" def __iter__(self):\n",
" return look_ahead_iterator(\n",
" (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file)))\n",
" )\n",
"\n",
"# Instantiate the dataset\n",
"train_dataset = Trigrams(train_file_path, vocab_size)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0cf7aa68-37aa-48a4-b647-e0e5002ca5c9",
"metadata": {},
"outputs": [],
"source": [
"class SimpleTrigramNeuralLanguageModel(nn.Module):\n",
" def __init__(self, vocabulary_size, embedding_size):\n",
" super(SimpleTrigramNeuralLanguageModel, self).__init__()\n",
" self.embedding = nn.Embedding(vocabulary_size, embedding_size)\n",
" self.linear1 = nn.Linear(embedding_size * 2, embedding_size)\n",
" self.linear2 = nn.Linear(embedding_size, vocabulary_size)\n",
" self.softmax = nn.Softmax(dim=1)\n",
" self.embedding_size = embedding_size\n",
"\n",
" def forward(self, x):\n",
" embeds = self.embedding(x).view(x.size(0), -1)\n",
" out = self.linear1(embeds)\n",
" out = self.linear2(out)\n",
" return self.softmax(out)\n",
"\n",
"model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size).to(device)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "32ea22db-7259-4549-a9d5-4781d9bc99bc",
"metadata": {},
"outputs": [],
"source": [
"data = DataLoader(train_dataset, batch_size=batch_size)\n",
"optimizer = torch.optim.Adam(model.parameters())\n",
"criterion = torch.nn.CrossEntropyLoss()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0858967e-5143-4253-921d-a009dbbdca27",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c3d8f9d5b178490899934860a55c2508",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Train loop: 0it [00:00, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 tensor(10.3631, device='cuda:0', grad_fn=)\n",
"5000 tensor(5.7081, device='cuda:0', grad_fn=)\n",
"10000 tensor(5.5925, device='cuda:0', grad_fn=)\n",
"15000 tensor(5.5097, device='cuda:0', grad_fn=)\n"
]
},
{
"data": {
"text/plain": [
"SimpleTrigramNeuralLanguageModel(\n",
" (embedding): Embedding(30000, 300)\n",
" (linear1): Linear(in_features=600, out_features=300, bias=True)\n",
" (linear2): Linear(in_features=300, out_features=30000, bias=True)\n",
" (softmax): Softmax(dim=1)\n",
")"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.train()\n",
"step = 0\n",
"for _ in range(num_epochs):\n",
" for x1,x2,y in tqdm(data, desc=\"Train loop\"):\n",
" y = y.to(device)\n",
" x = torch.cat((x1.unsqueeze(1),x2.unsqueeze(1)), dim=1).to(device)\n",
" optimizer.zero_grad()\n",
" ypredicted = model(x)\n",
" \n",
" loss = criterion(torch.log(ypredicted), y)\n",
" if step % 5000 == 0:\n",
" print(step, loss)\n",
" step += 1\n",
" loss.backward()\n",
" optimizer.step()\n",
" step = 0\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "da4d116c-beec-436d-84d8-577282507226",
"metadata": {},
"outputs": [],
"source": [
"def get_gap_candidates(words, n=10, vocab=train_dataset.vocab):\n",
" ixs = vocab(words)\n",
" ixs = torch.tensor(ixs).unsqueeze(0).to(device)\n",
"\n",
" out = model(ixs)\n",
" top = torch.topk(out[0], n)\n",
" top_indices = top.indices.tolist()\n",
" top_probs = top.values.tolist()\n",
" top_words = vocab.lookup_tokens(top_indices)\n",
" return list(zip(top_words, top_probs))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0cafd70a-29b3-4a49-b40f-b8ce3143084a",
"metadata": {},
"outputs": [],
"source": [
"def clean(text):\n",
" text = text.replace('-\\\\n', '').replace('\\\\n', ' ').replace('\\\\t', ' ')\n",
" text = re.sub(r'\\n', ' ', text)\n",
" text = re.sub(r'(?<=\\w)[,-](?=\\w)', '', text)\n",
" text = re.sub(r'\\s+', ' ', text)\n",
" text = re.sub(r'\\p{P}', '', text)\n",
" text = text.strip()\n",
" return text\n",
" \n",
"def predictor(prefix):\n",
" words = clean(prefix)\n",
" candidates = get_gap_candidates(words.strip().split(' ')[-2:])\n",
"\n",
" probs_sum = 0\n",
" output = ''\n",
" for word,prob in candidates:\n",
" if word == \"\":\n",
" continue\n",
" probs_sum += prob\n",
" output += f\"{word}:{prob} \"\n",
" output += f\":{1-probs_sum}\"\n",
"\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "965ebaf3-4c0b-4462-8ac5-4746ec9489ab",
"metadata": {},
"outputs": [],
"source": [
"def generate_result(input_path, output_path='out.tsv'):\n",
" lines = []\n",
" with open(input_path, encoding='utf-8') as f:\n",
" for line in f:\n",
" columns = line.split('\\t')\n",
" prefix = columns[6]\n",
" suffix = columns[7]\n",
" lines.append(prefix)\n",
"\n",
" with open(output_path, 'w', encoding='utf-8') as output_file:\n",
" for line in lines:\n",
" result = predictor(line)\n",
" output_file.write(result + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "80547ba7-9d01-4d2b-9e83-269919513de9",
"metadata": {},
"outputs": [],
"source": [
"generate_result('dev-0/in.tsv', output_path='dev-0/out.tsv')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}