Skip to content

Probing

Using Probing Tasks to Train a Classifier with Foundational Modelsâš‘

1. Select a Foundational Modelâš‘

Choose a foundational model suitable for your task, such as BERT or GPT, pre-trained on a large corpus.

2. Define Your Probing Taskâš‘

Identify relevant features for your classification task and design a probing task to highlight these features.

3. Extract Representationsâš‘

Generate embeddings from your dataset using the foundational model.

from transformers import AutoModel, AutoTokenizer

# Load pre-trained model and tokenizer
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Tokenize and generate embeddings
inputs = tokenizer("Your text input here", return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
embeddings = outputs.last_hidden_state

4. Apply a Probing Classifierâš‘

Train a lightweight classifier on the embeddings to map to your task's specific labels.

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# Assuming `embeddings` is a 2D numpy array and `labels` is your target labels
X_train, X_test, y_train, y_test = train_test_split(embeddings, labels, test_size=0.2)

# Train a probing classifier
clf = LogisticRegression()
clf.fit(X_train, y_train)

# Evaluate the classifier
score = clf.score(X_test, y_test)
print(f"Accuracy: {score}")

5. Fine-tuning (Optional)âš‘

Optionally, fine-tune the foundational model on your specific task for improved performance.

from transformers import Trainer, TrainingArguments

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,
    evaluate_during_training=True,
    logging_dir="./logs",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)

trainer.train()

6.Deploymentâš‘

Use the trained model for predictions on new, unseen data.

Benefits and Considerationsâš‘

  • Efficiency: Saves time and resources by leveraging pre-trained models.
  • Insightful: Offers insights into data features used for classification.
  • Flexibility: Adaptable to various tasks by changing the probing task.

Example Use Caseâš‘

For a text classification task identifying toxic comments, generate embeddings, design a probing task for toxicity features, and train a linear classifier on these embeddings.

Last update: 2024-10-23
Created: 2024-10-23