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sentiment-analysis/training/aclimdb-sentiment-training.ipynb: update

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Luc Bijl 2026-08-31 17:24:33 +02:00
parent 2327a88228
commit e1fbb3687f
1 changed files with 62 additions and 20 deletions
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sentiment-analysis/training/aclimdb-sentiment-training.ipynb
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@ -27,8 +27,8 @@
"import os\n",
"import pandas as pd\n",
"\n",
"imdb_train_dataset = \"aclimdb/train\"\n",
"imdb_test_dataset = \"aclimdb/test\"\n",
"imdb_train_dataset = 'aclimdb/train'\n",
"imdb_test_dataset = 'aclimdb/test'\n",
"\n",
"train_reviews = []\n",
"train_scores = []\n",
@ -118,7 +118,7 @@
"metadata": {},
"outputs": [],
"source": [
"print(f\"Length training set: {len(df_train)}\\nLength testing set: {len(df_test)}\")"
"print(f'Length training set: {len(df_train)}\\nLength testing set: {len(df_test)}')"
]
},
{
@ -136,31 +136,67 @@
"outputs": [],
"source": [
"import torch\n",
"from transformers import DistilBertTokenizerFast\n",
"from torch.utils.data import DataLoader\n",
"from transformers import DistilBertForSequenceClassification\n",
"from torch.utils.data import DataLoader, TensorDataset\n",
"from transformers import (\n",
" DistilBertTokenizerFast,\n",
" DistilBertForSequenceClassification\n",
")\n",
"\n",
"model_name = \"distilbert-base-uncased\"\n",
"# Set device cuda\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print('Using:', device)\n",
"\n",
"# Define model\n",
"model_name = 'distilbert-base-uncased'\n",
"tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)\n",
"\n",
"# Tokenize and encode the text data\n",
"train_encodings = tokenizer(df_train['text'].tolist(), truncation=True, padding=True, return_tensors='pt')\n",
"test_encodings = tokenizer(df_test['text'].tolist(), truncation=True, padding=True, return_tensors='pt')\n",
"train_encodings = tokenizer(\n",
" df_train['text'].tolist(), \n",
" truncation=True, \n",
" padding=True, \n",
" return_tensors='pt'\n",
")\n",
"\n",
"# Create data loaders\n",
"train_dataset = torch.utils.data.TensorDataset(\n",
"test_encodings = tokenizer(\n",
" df_test['text'].tolist(), \n",
" truncation=True, \n",
" padding=True, \n",
" return_tensors='pt'\n",
")\n",
"\n",
"# Create tensor dataset\n",
"train_dataset = TensorDataset(\n",
" train_encodings['input_ids'], \n",
" train_encodings['attention_mask'], \n",
" torch.tensor(df_train['s'].values, dtype=torch.float32)\n",
")\n",
"train_dataloader = DataLoader(train_dataset, batch_size=25, shuffle=True)\n",
"\n",
"test_dataset = torch.utils.data.TensorDataset(\n",
"test_dataset = TensorDataset(\n",
" test_encodings['input_ids'], \n",
" test_encodings['attention_mask'], \n",
" torch.tensor(df_test['s'].values, dtype=torch.float32)\n",
")\n",
"test_dataloader = DataLoader(test_dataset, batch_size=25, shuffle=False)"
"\n",
"# Create data loaders\n",
"if device == 'cuda':\n",
" en_pin_memory = True\n",
"else:\n",
" en_pin_memory = False\n",
"\n",
"train_dataloader = DataLoader(\n",
" train_dataset, \n",
" batch_size=10, \n",
" shuffle=True,\n",
" pin_memory=en_pin_memory\n",
")\n",
"\n",
"test_dataloader = DataLoader(\n",
" test_dataset, \n",
" batch_size=10, \n",
" shuffle=False,\n",
" pin_memory=en_pin_memory\n",
")"
]
},
{
@ -177,7 +213,10 @@
"metadata": {},
"outputs": [],
"source": [
"model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=1)"
"model = DistilBertForSequenceClassification.from_pretrained(\n",
" model_name, \n",
" num_labels=1\n",
").to(device)"
]
},
{
@ -235,7 +274,7 @@
" for batch in train_dataloader:\n",
" global_step += 1\n",
" num_batches += 1\n",
" input_ids, attention_mask, target_scores = batch\n",
" input_ids, attention_mask, target_scores = [x.to(device) for x in batch]\n",
"\n",
" # Forward pass\n",
" output = model(input_ids=input_ids, attention_mask=attention_mask)\n",
@ -270,7 +309,7 @@
"\n",
" for batch in test_dataloader:\n",
" with torch.no_grad():\n",
" input_ids, attention_mask, target_scores = batch\n",
" input_ids, attention_mask, target_scores = [x.to(device) for x in batch]\n",
"\n",
" # Obtaining the scores\n",
" output = model(input_ids=input_ids, attention_mask=attention_mask)\n",
@ -290,7 +329,7 @@
" print(f\"Epoch {epoch + 1}/{num_epochs}, Validation loss: {average_loss:.4f}\\n\")\n",
"\n",
" # Saving the model\n",
" torch.save(model, f'bert-aclimdb/{epoch + 1}.pth')\n",
" torch.save(model.state_dict(), f'bert-aclimdb/{epoch + 1}.pth')\n",
"\n",
" # Early stopping check\n",
" if average_loss < best_validation_loss:\n",
@ -300,6 +339,7 @@
" no_improvement_counter += 1\n",
"\n",
" if no_improvement_counter >= early_stop_patience:\n",
" print('Early stopping triggered')\n",
" break\n",
"\n",
"writer.close()"
@ -319,7 +359,9 @@
"metadata": {},
"outputs": [],
"source": [
"model = torch.load('bert-aclimdb/4.pth')"
"model = DistilBertForSequenceClassification.from_pretrained(model_name, num_labels=1)\n",
"model.load_state_dict(torch.load('...', map_location=device))\n",
"model.to(device)"
]
},
{
@ -344,7 +386,7 @@
"\n",
"for batch in test_dataloader:\n",
" with torch.no_grad():\n",
" input_ids, attention_mask, scores = batch\n",
" input_ids, attention_mask, scores = [x.to(device) for x in batch]\n",
"\n",
" # Obtaining the scores\n",
" output = model(input_ids=input_ids, attention_mask=attention_mask)\n",