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Proyecto 1: Análisis de Sentimientos con BERT
7. Paso 4: Ajuste Fino de BERT
Realizaremos un ajuste fino de un modelo BERT pre-entrenado para la clasificación binaria (sentimiento positivo o negativo).
Ejemplo de Código: Ajuste Fino
# 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. Paso 4: Ajuste Fino de BERT
Realizaremos un ajuste fino de un modelo BERT pre-entrenado para la clasificación binaria (sentimiento positivo o negativo).
Ejemplo de Código: Ajuste Fino
# 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. Paso 4: Ajuste Fino de BERT
Realizaremos un ajuste fino de un modelo BERT pre-entrenado para la clasificación binaria (sentimiento positivo o negativo).
Ejemplo de Código: Ajuste Fino
# 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. Paso 4: Ajuste Fino de BERT
Realizaremos un ajuste fino de un modelo BERT pre-entrenado para la clasificación binaria (sentimiento positivo o negativo).
Ejemplo de Código: Ajuste Fino
# 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()