Loading and inspecting pre-trained models

You're building a conversational AI assistant that can engage in human-like dialog across a wide range of topics, leveraging the powerful BERT model that has been pre-trained on a large corpus of text data.

You'll print the configuration to verify that you've loaded a conversational AI model with certain parameters like model_type: bert, num_attention_heads: 12, and num_hidden_layers: 12.

This exercise is part of the course

Efficient AI Model Training with PyTorch

View Course

Exercise instructions

Initialize the model parameters with the appropriate AutoModel class to load the bert-base-uncased model.
Print the model's configuration.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from transformers import AutoModelForSequenceClassification

# Load a pre-trained bert-base-uncased model
model = ____.____("bert-base-uncased")

# Print the model's configuration
print(model.____)

Edit and Run Code