"Here is the code to complete the assignment:
"
"`python\nimport copy\nimport torch\nfrom torch import nn\nfrom torch import optim\nimport torchtext\nfrom torchtext import data\nfrom torchtext import datasets\n\nTEXT = data.Field(sequential=True, batch_first=True, lower=True)\nLABEL = data.LabelField()\n\n# load data splits\ntrain_data, val_data, test_data = datasets.SST.splits(TEXT, LABEL)\n\n# build dictionary\nTEXT.build_vocab(train_data)\nLABEL.build_vocab(train_data)\n\n# hyperparameters\nvocab_size = len(TEXT.vocab)\nlabel_size = len(LABEL.vocab)\npadding_idx = TEXT.vocab.stoi['']\nembedding_dim = 128\nhidden_dim = 128\n\n# build iterators\ntrain_iter, val_iter, test_iter = data.BucketIterator.splits(\n (train_data, val_data, test_data), \n batch_size=32)\n\n# 1. Define the training and evaluation function\ndef train(model, train_iter, criterion, optimizer):\n model.train()\n total_loss = 0.0\n total_correct = 0\n\n for batch in train_iter:\n text, label = batch.text, batch.label\n optimizer.zero_grad()\n output = model(text)\n loss = criterion(output, label)\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n total_correct += (output.argmax(1) == label).sum().item()\n\n return total_loss / len(train_iter), total_correct / len(train_iter.dataset)\n\ndef evaluate(model, val_iter, criterion):\n model.eval()\n total_loss = 0.0\n total_correct = 0\n\n with torch.no_grad():\n for batch in val_iter:\n text, label = batch.text, batch.label\n output = model(text)\n loss = criterion(output, label)\n\n total_loss += loss.item()\n total_correct += (output.argmax(1) == label).sum().item()\n\n return total_loss / len(val_iter), total_correct / len(val_iter.dataset)\n\n# 2. Build a RNN model for sentiment analysis\nclass RNNClassifier(nn.Module):\n def init(self, vocab_size, embedding_dim, hidden_dim, label_size, padding_idx):\n super(RNNClassifier, self).init()\n self.vocab_size = vocab_size\n self.embedding_dim = embedding_dim\n self.hidden_dim = hidden_dim\n self.label_size = label_size\n self.num_layers = 1\n\n self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim, padding_idx=padding_idx)\n self.rnn = nn.RNN(self.embedding_dim, self.hidden_dim, batch_first=True)\n self.fc = nn.Linear(self.hidden_dim, self.label_size)\n\n def zero_state(self, batch_size):\n return torch.zeros(self.num_layers, batch_size, self.hidden_dim)\n\n def forward(self, text):\n embedding = self.embedding(text)\n hidden = self.zero_state(text.size(0))\n output, hidden = self.rnn(embedding, hidden)\n output = self.fc(output[:, -1, :])\n return output\n\n# 3. Train the model and compute the accuracy\nmodel = RNNClassifier(vocab_size, embedding_dim, hidden_dim, label_size, padding_idx)\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters())\n\nnum_epochs = 10\nbest_val_acc = 0.0\n\nfor epoch in range(num_epochs):\n train_loss, train_acc = train(model, train_iter, criterion, optimizer)\n val_loss, val_acc = evaluate(model, val_iter, criterion)\n\n print(f"Epoch {epoch+1}/{num_epochs}:")\n print(f"Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.4f}")\n print(f"Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f}")\n\n if val_acc > best_val_acc:\n best_val_acc = val_acc\n best_model = copy.deepcopy(model)\n\n# 4. Train a model with better accuracy\n# You can experiment with different optimizers, learning rates, hidden layer dimensions, etc.\n# For example, you can try using SGD optimizer and increasing the hidden_dim.\n\nmodel = RNNClassifier(vocab_size, embedding_dim, hidden_dim, label_size, padding_idx)\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01)\n\nnum_epochs = 10\nbest_val_acc = 0.0\n\nfor epoch in range(num_epochs):\n train_loss, train_acc = train(model, train_iter, criterion, optimizer)\n val_loss, val_acc = evaluate(model, val_iter, criterion)\n\n print(f"Epoch {epoch+1}/{num_epochs}:")\n print(f"Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.4f}")\n print(f"Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f}")\n\n if val_acc > best_val_acc:\n best_val_acc = val_acc\n best_model = copy.deepcopy(model)\n`"\n\nYou can play around with different hyperparameters and optimizers to try to achieve a higher accuracy.
