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Project 1: Sentiment Analysis with BERT
7. Step 4: Fine-Tuning BERT
We’ll fine-tune a pre-trained BERT model for binary classification (positive or negative sentiment).
Code Example: Fine-Tuning
# Load pre-trained BERT model
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_test,
)
# Train the model
trainer.train()7. Step 4: Fine-Tuning BERT
We’ll fine-tune a pre-trained BERT model for binary classification (positive or negative sentiment).
Code Example: Fine-Tuning
# Load pre-trained BERT model
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_test,
)
# Train the model
trainer.train()7. Step 4: Fine-Tuning BERT
We’ll fine-tune a pre-trained BERT model for binary classification (positive or negative sentiment).
Code Example: Fine-Tuning
# Load pre-trained BERT model
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_test,
)
# Train the model
trainer.train()7. Step 4: Fine-Tuning BERT
We’ll fine-tune a pre-trained BERT model for binary classification (positive or negative sentiment).
Code Example: Fine-Tuning
# Load pre-trained BERT model
model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_test,
)
# Train the model
trainer.train()