It’s a pre-trained language model that can be fine-tuned on specific tasks like question answering or sentiment analysis.
Now the configuration class. This is where all the magic happens! The config file contains all the parameters for BERT, such as the number of layers and hidden units (which are basically fancy math terms that make it sound more complicated than it actually is).
For example, if you want to use a pre-trained model called “bert-base-uncased” (which means it’s trained on unprocessed text), you can load the configuration like this:
# Import the necessary libraries
from transformers import AutoTokenizer, BertForNextSentencePrediction
import torch
# Create a tokenizer object using the AutoTokenizer class and load the pre-trained model "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Create a model object using the BertForNextSentencePrediction class and load the pre-trained model "bert-base-uncased"
model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased")
This will load the pre-trained weights and configuration for BERT, but it won’t actually run any predictions yet (we’ll get to that in a bit).
Now let’s say you have some text like “In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.” And you want to predict whether the next sentence will continue with something related to food or not. You can do this by passing both sentences through BERT and getting it to predict if they’re likely to be connected:
# Importing necessary libraries
import torch
from transformers import BertTokenizer, BertForNextSentencePrediction
# Initializing the BERT tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
# Defining the prompt and next sentence
prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
next_sentence = "The sky is blue due to the shorter wavelength of blue light."
# Encoding the prompt and next sentence using the BERT tokenizer
encoding = tokenizer(prompt, next_sentence, return_tensors="pt")
# Passing the encoded input to the BERT model and getting the outputs
outputs = model(**encoding)
# Extracting the logits (probabilities) for the two possible outcomes - related to food or not
logits = outputs.logits
# Asserting that the probability of the second sentence being related to food is lower than the probability of it not being related
assert logits[0, 0] < logits[0, 1] # This line checks if the probability of the second sentence being related to food is lower than the probability of it not being related (which would be true in this case since we're comparing "pizza" and "sky")
So that’s a basic overview of how BERT works with its configuration class. It might seem complicated at first, but once you get the hang of it, it can really help improve your machine learning models!