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人工智能系统实战第三期/实战代码/自然语言处理/基于FNN、RNN和Transformer的词性标注实战/Fine_tuning_Pretrained_Transformers_for_PoS_Tagging.ipynb 0 → 100644
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"text": [
"Collecting torchtext==0.5.0\n",
" Downloading torchtext-0.5.0-py3-none-any.whl (73 kB)\n",
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/73.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.2/73.2 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torchtext==0.5.0) (4.66.1)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchtext==0.5.0) (2.31.0)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from torchtext==0.5.0) (2.1.0+cu121)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchtext==0.5.0) (1.23.5)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from torchtext==0.5.0) (1.16.0)\n",
"Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from torchtext==0.5.0) (0.1.99)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchtext==0.5.0) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchtext==0.5.0) (3.6)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchtext==0.5.0) (2.0.7)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchtext==0.5.0) (2024.2.2)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->torchtext==0.5.0) (3.13.1)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->torchtext==0.5.0) (4.9.0)\n",
"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->torchtext==0.5.0) (1.12)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->torchtext==0.5.0) (3.2.1)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->torchtext==0.5.0) (3.1.3)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->torchtext==0.5.0) (2023.6.0)\n",
"Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch->torchtext==0.5.0) (2.1.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->torchtext==0.5.0) (2.1.5)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->torchtext==0.5.0) (1.3.0)\n",
"Installing collected packages: torchtext\n",
" Attempting uninstall: torchtext\n",
" Found existing installation: torchtext 0.16.0\n",
" Uninstalling torchtext-0.16.0:\n",
" Successfully uninstalled torchtext-0.16.0\n",
"Successfully installed torchtext-0.5.0\n"
]
}
],
"source": [
"!pip install torchtext==0.5.0"
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim\n",
"\n",
"from torchtext import data,datasets\n",
"\n",
"from transformers import BertTokenizer, BertModel\n",
"\n",
"import numpy as np\n",
"\n",
"import time\n",
"import random\n",
"import functools"
],
"metadata": {
"id": "mQQQwIPpYb2g"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"SEED = 1234\n",
"\n",
"random.seed(SEED)\n",
"np.random.seed(SEED)\n",
"torch.manual_seed(SEED)\n",
"torch.backends.cudnn.deterministic = True"
],
"metadata": {
"id": "lzJeqA9hYfMF"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
],
"metadata": {
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"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
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"tokenizer_config.json: 0%| | 0.00/28.0 [00:00<?, ?B/s]"
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{
"output_type": "display_data",
"data": {
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"vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
],
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},
{
"cell_type": "code",
"source": [
"init_token = tokenizer.cls_token\n",
"pad_token = tokenizer.pad_token\n",
"unk_token = tokenizer.unk_token\n",
"\n",
"print(init_token, pad_token, unk_token)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "O9W6TyKqYpOQ",
"outputId": "51201b17-2e70-45fb-df04-afa57446de52"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[CLS] [PAD] [UNK]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"init_token_idx = tokenizer.convert_tokens_to_ids(init_token)\n",
"pad_token_idx = tokenizer.convert_tokens_to_ids(pad_token)\n",
"unk_token_idx = tokenizer.convert_tokens_to_ids(unk_token)\n",
"\n",
"print(init_token_idx, pad_token_idx, unk_token_idx)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "r2Rra5YpYrw4",
"outputId": "0f73c6e6-0a94-4346-a180-88d47bd72f69"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"101 0 100\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"max_input_length = tokenizer.max_model_input_sizes['bert-base-uncased']\n",
"\n",
"print(max_input_length)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eofWHi-iYtVg",
"outputId": "1b883477-2e0c-480b-e325-3d06fd06183b"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"512\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def cut_and_convert_to_id(tokens, tokenizer, max_input_length):\n",
" tokens = tokens[:max_input_length-1]\n",
" tokens = tokenizer.convert_tokens_to_ids(tokens)\n",
" return tokens"
],
"metadata": {
"id": "VQuyJCxNYvBX"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def cut_to_max_length(tokens, max_input_length):\n",
" tokens = tokens[:max_input_length-1]\n",
" return tokens"
],
"metadata": {
"id": "TSwUvs-oYwni"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"text_preprocessor = functools.partial(cut_and_convert_to_id,\n",
" tokenizer = tokenizer,\n",
" max_input_length = max_input_length)\n",
"\n",
"tag_preprocessor = functools.partial(cut_to_max_length,\n",
" max_input_length = max_input_length)"
],
"metadata": {
"id": "iQ8aZAT6Yx4W"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"TEXT = data.Field(use_vocab = False,\n",
" lower = True,\n",
" preprocessing = text_preprocessor,\n",
" init_token = init_token_idx,\n",
" pad_token = pad_token_idx,\n",
" unk_token = unk_token_idx)\n",
"\n",
"UD_TAGS = data.Field(unk_token = None,\n",
" init_token = '<pad>',\n",
" preprocessing = tag_preprocessor)"
],
"metadata": {
"id": "QITICh3zYzYY"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"fields = ((\"text\", TEXT), (\"udtags\", UD_TAGS))"
],
"metadata": {
"id": "W78x96mwY1Ik"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train_data, valid_data, test_data = datasets.UDPOS.splits(fields)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9zKBEaKJY22f",
"outputId": "89520af0-11f5-4a6a-c2fb-44d3052dba36"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"downloading en-ud-v2.zip\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"en-ud-v2.zip: 100%|██████████| 688k/688k [00:00<00:00, 1.61MB/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"extracting\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(vars(train_data.examples[0]))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "neQhD6n8Y4Q2",
"outputId": "ea0ef77f-392c-4d0e-860b-c392df575ce2"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'text': [2632, 1011, 100, 1024, 2137, 2749, 2730, 100, 14093, 2632, 1011, 100, 1010, 1996, 14512, 2012, 1996, 8806, 1999, 1996, 2237, 1997, 100, 1010, 2379, 1996, 9042, 3675, 1012], 'udtags': ['PROPN', 'PUNCT', 'PROPN', 'PUNCT', 'ADJ', 'NOUN', 'VERB', 'PROPN', 'PROPN', 'PROPN', 'PUNCT', 'PROPN', 'PUNCT', 'DET', 'NOUN', 'ADP', 'DET', 'NOUN', 'ADP', 'DET', 'NOUN', 'ADP', 'PROPN', 'PUNCT', 'ADP', 'DET', 'ADJ', 'NOUN', 'PUNCT']}\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"UD_TAGS.build_vocab(train_data)\n",
"\n",
"print(UD_TAGS.vocab.stoi)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6TAcJEuzY6gi",
"outputId": "2c163050-c01c-4c96-bb2e-3575944bcbfb"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"defaultdict(None, {'<pad>': 0, 'NOUN': 1, 'PUNCT': 2, 'VERB': 3, 'PRON': 4, 'ADP': 5, 'DET': 6, 'PROPN': 7, 'ADJ': 8, 'AUX': 9, 'ADV': 10, 'CCONJ': 11, 'PART': 12, 'NUM': 13, 'SCONJ': 14, 'X': 15, 'INTJ': 16, 'SYM': 17})\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"BATCH_SIZE = 32\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n",
" (train_data, valid_data, test_data),\n",
" batch_size = BATCH_SIZE,\n",
" device = device)"
],
"metadata": {
"id": "Qhe-y7NQY8CT"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class BERTPoSTagger(nn.Module):\n",
" def __init__(self,\n",
" bert,\n",
" output_dim,\n",
" dropout):\n",
"\n",
" super().__init__()\n",
"\n",
" self.bert = bert\n",
"\n",
" embedding_dim = bert.config.to_dict()['hidden_size']\n",
"\n",
" self.fc = nn.Linear(embedding_dim, output_dim)\n",
"\n",
" self.dropout = nn.Dropout(dropout)\n",
"\n",
" def forward(self, text):\n",
"\n",
" #text = [sent len, batch size]\n",
"\n",
" text = text.permute(1, 0)\n",
"\n",
" #text = [batch size, sent len]\n",
"\n",
" embedded = self.dropout(self.bert(text)[0])\n",
"\n",
" #embedded = [batch size, seq len, emb dim]\n",
"\n",
" embedded = embedded.permute(1, 0, 2)\n",
"\n",
" #embedded = [sent len, batch size, emb dim]\n",
"\n",
" predictions = self.fc(self.dropout(embedded))\n",
"\n",
" #predictions = [sent len, batch size, output dim]\n",
"\n",
" return predictions"
],
"metadata": {
"id": "yef_QNp0Y9hF"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"source": [
"bert = BertModel.from_pretrained('bert-base-uncased')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"f3fd1a6130eb4abfb7abbc2accc22672",
"7abf6e6880524922b73953f84a3928bd",
"2059f4631bc7483d8da296f19f53598d",
"1e167814d5384583a1ae6a069fbc9598",
"5c92ad97c733448a8d64f2c28b6506e9",
"bb0d9b1e6a5f4c80853bc6448f9152f0",
"52b79e57f95c4cc8819f801f3ce20f81",
"64368a0f850546cfa485b6490a30cf2a",
"d2fb25a6732a4299b810fa21c82ff03e",
"e439e171af6c4a5fa950ec32128f0b97",
"e50bb99c3a444f66b2208a91dce68d89"
]
},
"id": "nsAWFIG_Y_rd",
"outputId": "84630339-d24d-4f87-8113-9dcdd34b7e24"
},
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/440M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f3fd1a6130eb4abfb7abbc2accc22672"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"OUTPUT_DIM = len(UD_TAGS.vocab)\n",
"DROPOUT = 0.25\n",
"\n",
"model = BERTPoSTagger(bert,\n",
" OUTPUT_DIM,\n",
" DROPOUT)"
],
"metadata": {
"id": "C4d7mqGMZByG"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def count_parameters(model):\n",
" return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
"\n",
"print(f'The model has {count_parameters(model):,} trainable parameters')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T1fPO3NrZEO5",
"outputId": "3981a9da-5d9a-4c53-a499-5d5f545238e9"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The model has 109,496,082 trainable parameters\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"LEARNING_RATE = 5e-5\n",
"\n",
"optimizer = optim.Adam(model.parameters(), lr = LEARNING_RATE)"
],
"metadata": {
"id": "vuhpt9nCZGHe"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"TAG_PAD_IDX = UD_TAGS.vocab.stoi[UD_TAGS.pad_token]\n",
"\n",
"criterion = nn.CrossEntropyLoss(ignore_index = TAG_PAD_IDX)"
],
"metadata": {
"id": "sCYtbbCHZH3g"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = model.to(device)\n",
"criterion = criterion.to(device)"
],
"metadata": {
"id": "oYQon7MqZJRB"
},
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def categorical_accuracy(preds, y, tag_pad_idx):\n",
" \"\"\"\n",
" Returns accuracy per batch, i.e. if you get 8/10 right, this returns 0.8, NOT 8\n",
" \"\"\"\n",
" max_preds = preds.argmax(dim = 1, keepdim = True) # get the index of the max probability\n",
" non_pad_elements = (y != tag_pad_idx).nonzero()\n",
" correct = max_preds[non_pad_elements].squeeze(1).eq(y[non_pad_elements])\n",
" return correct.sum() / torch.FloatTensor([y[non_pad_elements].shape[0]]).to(device)"
],
"metadata": {
"id": "i2VsbTmgZKw6"
},
"execution_count": 24,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def train(model, iterator, optimizer, criterion, tag_pad_idx):\n",
"\n",
" epoch_loss = 0\n",
" epoch_acc = 0\n",
"\n",
" model.train()\n",
"\n",
" for batch in iterator:\n",
"\n",
" text = batch.text\n",
" tags = batch.udtags\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" #text = [sent len, batch size]\n",
"\n",
" predictions = model(text)\n",
"\n",
" #predictions = [sent len, batch size, output dim]\n",
" #tags = [sent len, batch size]\n",
"\n",
" predictions = predictions.view(-1, predictions.shape[-1])\n",
" tags = tags.view(-1)\n",
"\n",
" #predictions = [sent len * batch size, output dim]\n",
" #tags = [sent len * batch size]\n",
"\n",
" loss = criterion(predictions, tags)\n",
"\n",
" acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n",
"\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" epoch_loss += loss.item()\n",
" epoch_acc += acc.item()\n",
"\n",
" return epoch_loss / len(iterator), epoch_acc / len(iterator)"
],
"metadata": {
"id": "51NFGC1eZMYX"
},
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def evaluate(model, iterator, criterion, tag_pad_idx):\n",
"\n",
" epoch_loss = 0\n",
" epoch_acc = 0\n",
"\n",
" model.eval()\n",
"\n",
" with torch.no_grad():\n",
"\n",
" for batch in iterator:\n",
"\n",
" text = batch.text\n",
" tags = batch.udtags\n",
"\n",
" predictions = model(text)\n",
"\n",
" predictions = predictions.view(-1, predictions.shape[-1])\n",
" tags = tags.view(-1)\n",
"\n",
" loss = criterion(predictions, tags)\n",
"\n",
" acc = categorical_accuracy(predictions, tags, tag_pad_idx)\n",
"\n",
" epoch_loss += loss.item()\n",
" epoch_acc += acc.item()\n",
"\n",
" return epoch_loss / len(iterator), epoch_acc / len(iterator)"
],
"metadata": {
"id": "ILVWUnwBZPO4"
},
"execution_count": 26,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def epoch_time(start_time, end_time):\n",
" elapsed_time = end_time - start_time\n",
" elapsed_mins = int(elapsed_time / 60)\n",
" elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
" return elapsed_mins, elapsed_secs"
],
"metadata": {
"id": "FY17XjJYZRFz"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"N_EPOCHS = 10\n",
"\n",
"best_valid_loss = float('inf')\n",
"\n",
"for epoch in range(N_EPOCHS):\n",
"\n",
" start_time = time.time()\n",
"\n",
" train_loss, train_acc = train(model, train_iterator, optimizer, criterion, TAG_PAD_IDX)\n",
" valid_loss, valid_acc = evaluate(model, valid_iterator, criterion, TAG_PAD_IDX)\n",
"\n",
" end_time = time.time()\n",
"\n",
" epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
"\n",
" if valid_loss < best_valid_loss:\n",
" best_valid_loss = valid_loss\n",
" torch.save(model.state_dict(), 'tut2-model.pt')\n",
"\n",
" print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
" print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
" print(f'\\t Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}%')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "44khueucZS1J",
"outputId": "dd537416-5fde-4883-f4a5-8b6807bd62f5"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 01 | Epoch Time: 0m 40s\n",
"\tTrain Loss: 0.406 | Train Acc: 88.34%\n",
"\t Val. Loss: 0.309 | Val. Acc: 90.70%\n",
"Epoch: 02 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.117 | Train Acc: 96.64%\n",
"\t Val. Loss: 0.282 | Val. Acc: 91.23%\n",
"Epoch: 03 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.076 | Train Acc: 97.78%\n",
"\t Val. Loss: 0.254 | Val. Acc: 92.15%\n",
"Epoch: 04 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.053 | Train Acc: 98.47%\n",
"\t Val. Loss: 0.267 | Val. Acc: 92.33%\n",
"Epoch: 05 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.039 | Train Acc: 98.87%\n",
"\t Val. Loss: 0.282 | Val. Acc: 92.36%\n",
"Epoch: 06 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.029 | Train Acc: 99.19%\n",
"\t Val. Loss: 0.296 | Val. Acc: 91.75%\n",
"Epoch: 07 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.025 | Train Acc: 99.27%\n",
"\t Val. Loss: 0.288 | Val. Acc: 92.61%\n",
"Epoch: 08 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.021 | Train Acc: 99.38%\n",
"\t Val. Loss: 0.342 | Val. Acc: 91.88%\n",
"Epoch: 09 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.020 | Train Acc: 99.40%\n",
"\t Val. Loss: 0.323 | Val. Acc: 91.68%\n",
"Epoch: 10 | Epoch Time: 0m 37s\n",
"\tTrain Loss: 0.015 | Train Acc: 99.55%\n",
"\t Val. Loss: 0.329 | Val. Acc: 91.86%\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"model.load_state_dict(torch.load('tut2-model.pt'))\n",
"\n",
"test_loss, test_acc = evaluate(model, test_iterator, criterion, TAG_PAD_IDX)\n",
"\n",
"print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PjQ9S1PhZU2H",
"outputId": "78d13174-9e45-4020-a105-ec018b078135"
},
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test Loss: 0.281 | Test Acc: 90.85%\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def tag_sentence(model, device, sentence, tokenizer, text_field, tag_field):\n",
"\n",
" model.eval()\n",
"\n",
" if isinstance(sentence, str):\n",
" tokens = tokenizer.tokenize(sentence)\n",
" else:\n",
" tokens = sentence\n",
"\n",
" numericalized_tokens = tokenizer.convert_tokens_to_ids(tokens)\n",
" numericalized_tokens = [text_field.init_token] + numericalized_tokens\n",
"\n",
" unk_idx = text_field.unk_token\n",
"\n",
" unks = [t for t, n in zip(tokens, numericalized_tokens) if n == unk_idx]\n",
"\n",
" token_tensor = torch.LongTensor(numericalized_tokens)\n",
"\n",
" token_tensor = token_tensor.unsqueeze(-1).to(device)\n",
"\n",
" predictions = model(token_tensor)\n",
"\n",
" top_predictions = predictions.argmax(-1)\n",
"\n",
" predicted_tags = [tag_field.vocab.itos[t.item()] for t in top_predictions]\n",
"\n",
" predicted_tags = predicted_tags[1:]\n",
"\n",
" assert len(tokens) == len(predicted_tags)\n",
"\n",
" return tokens, predicted_tags, unks"
],
"metadata": {
"id": "E11tUcSeZXMj"
},
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"sentence = 'The Queen will deliver a speech about the conflict in North Korea at 1pm tomorrow.'\n",
"\n",
"tokens, tags, unks = tag_sentence(model,\n",
" device,\n",
" sentence,\n",
" tokenizer,\n",
" TEXT,\n",
" UD_TAGS)\n",
"\n",
"print(unks)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nsPkcCmfZZWU",
"outputId": "54bd77f2-2db3-4071-8bfa-c99eb2014dc9"
},
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(\"Pred. Tag\\tToken\\n\")\n",
"\n",
"for token, tag in zip(tokens, tags):\n",
" print(f\"{tag}\\t\\t{token}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "z2c-XT5jZbM4",
"outputId": "8a750de6-1bd8-434c-a657-d8e3cb6722a4"
},
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Pred. Tag\tToken\n",
"\n",
"DET\t\tthe\n",
"NOUN\t\tqueen\n",
"AUX\t\twill\n",
"VERB\t\tdeliver\n",
"DET\t\ta\n",
"NOUN\t\tspeech\n",
"ADP\t\tabout\n",
"DET\t\tthe\n",
"NOUN\t\tconflict\n",
"ADP\t\tin\n",
"PROPN\t\tnorth\n",
"PROPN\t\tkorea\n",
"ADP\t\tat\n",
"NUM\t\t1\n",
"NOUN\t\t##pm\n",
"NOUN\t\ttomorrow\n",
"PUNCT\t\t.\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "tr_U6mN0ZdHT"
},
"execution_count": 32,
"outputs": []
}
]
}
\ No newline at end of file
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