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pytorch-sentiment-analysis/torchtext_0_10_imdb_nbow.ipynb

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added torchtext 0.10 imdb on nbow
2021-07-07 21:43:27 +08:00
{
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{
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{
"cell_type": "code",
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"metadata": {
"id": "Y2upWg_Qvax1"
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"outputs": [],
"source": [
"import functools\n",
"\n",
"import datasets\n",
"\n",
"import torchtext\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim"
]
},
{
"cell_type": "code",
"execution_count": 2,
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"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.02 MiB, post-processed: Unknown size, total: 207.25 MiB) to /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a...\n"
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"Downloading: 0%| | 0.00/84.1M [00:00<?, ?B/s]"
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"0 examples [00:00, ? examples/s]"
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"text": [
"Dataset imdb downloaded and prepared to /home/ben/.cache/huggingface/datasets/imdb/plain_text/1.0.0/e3c66f1788a67a89c7058d97ff62b6c30531e05b549de56d3ab91891f0561f9a. Subsequent calls will reuse this data.\n"
]
}
],
"source": [
"train_data, test_data = datasets.load_dataset('imdb', split=['train', 'test'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "2f6USIQOvkV_",
"outputId": "3444d1ad-e2eb-4766-92d8-ea4d85f7011f"
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"outputs": [
{
"data": {
"text/plain": [
"(Dataset({\n",
" features: ['label', 'text'],\n",
" num_rows: 25000\n",
" }),\n",
" Dataset({\n",
" features: ['label', 'text'],\n",
" num_rows: 25000\n",
" }))"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"train_data, test_data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "3Av1UUtZxoSL"
},
"outputs": [],
"source": [
"tokenizer = torchtext.data.utils.get_tokenizer('basic_english')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "ju4G0eKEx2RO"
},
"outputs": [],
"source": [
"def tokenize_data(example, tokenizer):\n",
" tokens = {'tokens': tokenizer(example['text'])}\n",
" return tokens"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
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],
"source": [
"train_data = train_data.map(tokenize_data, fn_kwargs={'tokenizer': tokenizer})\n",
"test_data = test_data.map(tokenize_data, fn_kwargs={'tokenizer': tokenizer})"
]
},
{
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"(Dataset({\n",
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" num_rows: 25000\n",
" }))"
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"execution_count": 7,
"metadata": {},
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"source": [
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},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "OfzYwhN2wFcA"
},
"outputs": [],
"source": [
"train_valid_data = train_data.train_test_split(test_size=0.25)\n",
"train_data = train_valid_data['train']\n",
"valid_data = train_valid_data['test']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ovGkrOJkwZsC",
"outputId": "b5ca62c6-96e5-4eca-8c0f-558fd087dee1"
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"outputs": [
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"(18750, 6250, 25000)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_data), len(valid_data), len(test_data)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "9VMeQG_FxUVO"
},
"outputs": [],
"source": [
"min_freq = 3\n",
"special_tokens = ['<unk>', '<pad>']\n",
"\n",
"vocab = torchtext.vocab.build_vocab_from_iterator(train_data['tokens'],\n",
" min_freq=min_freq,\n",
" specials=special_tokens)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rbroBAClxXGB",
"outputId": "91c5da92-7f97-4ad8-a946-6de1899e64a2"
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"outputs": [
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"data": {
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"35341"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(vocab)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3bKHqCxPyQSb",
"outputId": "35b3c437-f0f8-43fb-8f10-b968c597d5b4"
},
"outputs": [
{
"data": {
"text/plain": [
"['<unk>', '<pad>', 'the', '.', ',', 'and', 'a', 'of', 'to', \"'\"]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vocab.get_itos()[:10]"
]
},
{
"cell_type": "code",
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"metadata": {
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"base_uri": "https://localhost:8080/"
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},
"execution_count": 13,
"metadata": {},
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],
"source": [
"unk_index = vocab['<unk>']\n",
"\n",
"unk_index"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
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"outputId": "46666a6d-56ff-42e3-ebe2-6f87930270c7"
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"1"
]
},
"execution_count": 14,
"metadata": {},
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}
],
"source": [
"pad_index = vocab['<pad>']\n",
"\n",
"pad_index"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "_syj_YR8yp7B"
},
"outputs": [],
"source": [
"vocab.set_default_index(unk_index)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "ENlE1eAM0lHe"
},
"outputs": [],
"source": [
"def numericalize_data(example, vocab):\n",
" ids = {'ids': [vocab[token] for token in example['tokens']]}\n",
" return ids"
]
},
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],
"source": [
"train_data = train_data.map(numericalize_data, fn_kwargs={'vocab': vocab})\n",
"valid_data = valid_data.map(numericalize_data, fn_kwargs={'vocab': vocab})\n",
"test_data = test_data.map(numericalize_data, fn_kwargs={'vocab': vocab})"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "GAXGqlXT1FD9"
},
"outputs": [],
"source": [
"train_data.set_format(type='torch', columns=['ids', 'label'])\n",
"valid_data.set_format(type='torch', columns=['ids', 'label'])\n",
"test_data.set_format(type='torch', columns=['ids', 'label'])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "qCmyoFKAvmnj"
},
"outputs": [],
"source": [
"class NBoW(nn.Module):\n",
" def __init__(self, vocab_size, embedding_dim, output_dim, pad_index):\n",
" super().__init__()\n",
" self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_index)\n",
" self.fc = nn.Linear(embedding_dim, output_dim)\n",
"\n",
" def forward(self, text):\n",
" # text = [batch size, seq len]\n",
" embedded = self.embedding(text)\n",
" # embedded = [batch size, seq len, embedding dim]\n",
" pooled = embedded.mean(dim=1)\n",
" # pooled = [batch size, embedding dim]\n",
" prediction = self.fc(pooled)\n",
" # prediction = [batch size, output dim]\n",
" return prediction"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "lPym0qxrwC7e"
},
"outputs": [],
"source": [
"vocab_size = len(vocab)\n",
"embedding_dim = 256\n",
"output_dim = 2\n",
"\n",
"model = NBoW(vocab_size, embedding_dim, output_dim, pad_index)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Prvx6C3TFyI4",
"outputId": "c9069fbe-ec6e-423d-ff59-820f934c6432"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
".vector_cache/wiki.en.vec: 7%|▋ | 465M/6.60G [08:18<3:21:18, 508kB/s] "
]
}
],
"source": [
"vectors = torchtext.vocab.FastText()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e3gI343FIETN",
"outputId": "44ed85d4-3e22-4bf0-e86e-0d7c01e33c35"
},
"outputs": [],
"source": [
"vectors = torchtext.vocab.GloVe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xLg8TKFCzAfL"
},
"outputs": [],
"source": [
"optimizer = optim.Adam(model.parameters())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XJrIlwlfzQqY"
},
"outputs": [],
"source": [
"criterion = nn.CrossEntropyLoss()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KbEREGWmzR7J",
"outputId": "44ec5013-ab39-4f35-b425-250f7e4093ef"
},
"outputs": [],
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"device"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IBtd-0I3zVTo"
},
"outputs": [],
"source": [
"model = model.to(device)\n",
"criterion = criterion.to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TNaDUz3M2QDM"
},
"outputs": [],
"source": [
"def collate(batch, pad_index):\n",
" batch_ids = [i['ids'] for i in batch]\n",
" batch_ids = nn.utils.rnn.pad_sequence(batch_ids, padding_value=pad_index, batch_first=True)\n",
" batch_labels = [i['label'] for i in batch]\n",
" batch_labels = torch.stack(batch_labels)\n",
" batch = {'ids': batch_ids,\n",
" 'labels': batch_labels}\n",
" return batch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LYsAzjrV0AnH"
},
"outputs": [],
"source": [
"batch_size = 512\n",
"\n",
"collate = functools.partial(collate, pad_index=pad_index)\n",
"\n",
"train_dataloader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, collate_fn=collate)\n",
"valid_dataloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size, collate_fn=collate)\n",
"test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, collate_fn=collate)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mKeLtjK5zX41"
},
"outputs": [],
"source": [
"def train(dataloader, model, criterion, optimizer, device):\n",
"\n",
" model.train()\n",
" epoch_loss = 0\n",
" epoch_accuracy = 0\n",
"\n",
" for batch in dataloader:\n",
" tokens = batch['ids'].to(device)\n",
" labels = batch['labels'].to(device)\n",
" predictions = model(tokens)\n",
" loss = criterion(predictions, labels)\n",
" accuracy = get_accuracy(predictions, labels)\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
" epoch_loss += loss.item()\n",
" epoch_accuracy += accuracy.item()\n",
"\n",
" return epoch_loss / len(dataloader), epoch_accuracy / len(dataloader)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3gJHwUZZ0NC6"
},
"outputs": [],
"source": [
"def evaluate(dataloader, model, criterion, device):\n",
" \n",
" model.eval()\n",
" epoch_loss = 0\n",
" epoch_accuracy = 0\n",
"\n",
" with torch.no_grad():\n",
" for batch in dataloader:\n",
" tokens = batch['ids'].to(device)\n",
" labels = batch['labels'].to(device)\n",
" predictions = model(tokens)\n",
" loss = criterion(predictions, labels)\n",
" accuracy = get_accuracy(predictions, labels)\n",
" epoch_loss += loss.item()\n",
" epoch_accuracy += accuracy.item()\n",
"\n",
" return epoch_loss / len(dataloader), epoch_accuracy / len(dataloader)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DOPRg4Gg5L44"
},
"outputs": [],
"source": [
"def get_accuracy(predictions, labels):\n",
" batch_size = predictions.shape[0]\n",
" predicted_classes = predictions.argmax(1, keepdim=True)\n",
" correct_predictions = predicted_classes.eq(labels.view_as(predicted_classes)).sum()\n",
" accuracy = correct_predictions.float() / batch_size\n",
" return accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "y0T-FtNN0PO3",
"outputId": "4781c655-bd66-4326-df6d-0d9dbedbaac5"
},
"outputs": [],
"source": [
"n_epochs = 10\n",
"\n",
"for epoch in range(n_epochs):\n",
"\n",
" train_loss, train_acc = train(train_dataloader, model, criterion, optimizer, device)\n",
" valid_loss, valid_acc = evaluate(valid_dataloader, model, criterion, device)\n",
"\n",
" print(f'epoch: {epoch+1}')\n",
" print(f'train_loss: {train_loss:.3f}, train_acc: {train_acc:.3f}')\n",
" print(f'valid_loss: {valid_loss:.3f}, valid_acc: {valid_acc:.3f}')"
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