Beginner Explanation
Imagine you have a robot that can recognize different animals because it has seen thousands of pictures. Now, you want this robot to be really good at recognizing just cats and dogs. To do this, you show it many labeled pictures of cats and dogs, telling it which is which. This extra training helps the robot become an expert at distinguishing between these two animals. This process of teaching the robot with specific examples is called supervised fine-tuning.Technical Explanation
Supervised fine-tuning involves taking a pre-trained model (like BERT or ResNet) and training it further on a labeled dataset tailored to a specific task. The goal is to adapt the model’s learned features to improve performance on this new task. This is typically done by replacing the final layer of the model with a task-specific layer and retraining the model using a smaller learning rate. For example, in PyTorch, you can load a pre-trained model and fine-tune it as follows: “`python import torch from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments # Load pre-trained model model = AutoModelForSequenceClassification.from_pretrained(‘bert-base-uncased’, num_labels=2) # Define training arguments training_args = TrainingArguments( output_dir=’./results’, num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=64, warmup_steps=500, weight_decay=0.01, logging_dir=’./logs’, ) # Initialize Trainer with model and training arguments trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset) # Fine-tune the model trainer.train() “`Academic Context
Supervised fine-tuning is a critical component in the transfer learning paradigm, where a model trained on a large dataset is adapted to a smaller, task-specific dataset. This approach leverages the model’s pre-existing knowledge, often resulting in improved generalization and performance. The foundational work by Yosinski et al. (2014) demonstrated that fine-tuning can significantly enhance performance on various tasks. Additionally, research by Howard and Ruder (2018) introduced the Universal Language Model Fine-tuning (ULMFiT) technique, showcasing the effectiveness of fine-tuning in natural language processing. The mathematical underpinnings involve adjusting the model’s weights through backpropagation, minimizing a loss function defined for the specific task.Code Examples
Example 1:
import torch
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
# Load pre-trained model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
# Define training arguments
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',
)
# Initialize Trainer with model and training arguments
trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset)
# Fine-tune the model
trainer.train()Example 2:
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',Example 3:
import torch
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
# Load pre-trained model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)Example 4:
from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
# Load pre-trained model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
View Source: https://arxiv.org/abs/2511.16671v1