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.
Deze oefening maakt deel uit van de cursus
Efficient AI Model Training with PyTorch
Oefeninstructies
- Initialize the model parameters with the appropriate AutoModel class to load the
bert-base-uncasedmodel. - Print the model's configuration.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
from transformers import AutoModelForSequenceClassification
# Load a pre-trained bert-base-uncased model
model = ____.____("bert-base-uncased")
# Print the model's configuration
print(model.____)