First off, we have the `BertConfig` class which contains all of the parameters that make up our model. This includes things like the number of layers and hidden units in each layer, as well as the size of the input embeddings. For example:
# Import the BertConfig class from the transformers library
from transformers import BertConfig
# Create an instance of the BertConfig class and assign it to the variable 'config'
# Set the number of hidden layers to 12 and the number of attention heads to 16
config = BertConfig(num_hidden_layers=12, num_attention_heads=16)
# The BertConfig class contains all the parameters that make up our model
# These parameters include the number of layers and hidden units in each layer, as well as the size of the input embeddings
In this case, we’re creating a new `BertConfig` object with 12 layers and 16 attention heads. Pretty cool! But what do these numbers actually mean? Well, the number of hidden layers refers to how many times our input data will be processed through a series of mathematical operations before being outputted as a prediction. The more layers we have, the better our model should perform on complex tasks like sentiment analysis or text classification.
The `num_attention_heads` parameter is a bit trickier to explain. Essentially, it refers to how many different “views” of the input data are being processed at once by the attention mechanism in BERT. This can help improve performance on certain types of tasks where contextual information is important (like question answering or text completion).
Now that we’ve got our configuration set up, how to actually use it with some code examples! First off, we need to load the pre-trained weights for BERT using the `AutoTokenizer` and `BertForSequenceClassification` classes from the transformers library:
# Import the necessary libraries
from transformers import AutoTokenizer, BertForSequenceClassification
# Initialize the tokenizer with the pre-trained BERT model
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Initialize the model for sequence classification using the pre-trained BERT weights
model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
# The `AutoTokenizer` class is used to automatically select the appropriate tokenizer for the specified pre-trained model.
# In this case, we are using the "bert-base-uncased" model, which is a BERT model trained on lower-cased English text.
# The `BertForSequenceClassification` class is used to fine-tune the pre-trained BERT model for sequence classification tasks.
# In this case, we are using the "bert-base-uncased" model, which is a BERT model trained on lower-cased English text.
In this case, we’re loading the pre-trained weights for BERT with an input embedding size of 768 (which is pretty standard). Once our model and tokenizer are loaded, we can use them to make predictions on new data:
# Loading pre-trained weights for BERT with an input embedding size of 768
# and initializing the model and tokenizer
model = BertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
# Defining the prompt for the prediction
prompt = "What is the best way to cook a steak?"
# Tokenizing the prompt using the tokenizer and converting it to input_ids
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Making predictions on the input_ids using the model
outputs = model(input_ids)
# Extracting the logits from the outputs
logits = outputs.logits
# Finding the index of the highest probability prediction
prediction = torch.argmax(logits).item()
# Printing the prediction
print(prediction)
# Output: 1 (index of the word "best" in the vocabulary)
In this example, we’re using the `tokenizer` to convert our input prompt into a list of token IDs that can be fed into BERT for processing. We then pass these token IDs as an argument to the `model`, which returns a set of logits (probabilities) for each possible output class. Finally, we use the `argmax()` function to find the index with the highest probability and convert it back to an integer using the `item()` method.
And that’s pretty much all there is to it! With BERT, you can easily train models on a wide variety of NLP tasks like sentiment analysis, text classification, question answering, and more. So if you want to get started with this awesome library, head over to the official documentation for more information: https://huggingface.co/transformers/
Hope that helps! Let me know if you have any questions or need further clarification on anything.