Here’s how it works: first, you gather up a bunch of text data that your transformer will learn from. This could be anything from news articles to social media posts to scientific papers the more diverse and varied the data is, the better! Then, you pre-train your model on this dataset using some fancy algorithms and techniques (which we won’t go into here because it would take forever).

Once your transformer has been trained, you can use it offline by loading up its weights (basically a set of numbers that represent how the different parts of the model are connected to each other) and running it on new data. This is where things get really cool instead of having to send every single input through the internet and wait for a response, your transformer can do all the heavy lifting right there on your local machine!

Here’s an example: let’s say you have a dataset of 10 million news articles that you want to use to train your transformer. You pre-train it using some fancy algorithms and techniques (which we won’t go into here because it would take forever), and then save the resulting weights as a file called “my_transformer.h5”.

Now, let’s say you have a new article that you want to classify as either positive or negative. Instead of sending this input through the internet and waiting for a response from some far-off server, you can load up your pre-trained transformer using Python (or whatever programming language you prefer) like so:

# Import necessary libraries
from keras.models import load_model # Importing the load_model function from the keras.models library
import numpy as np # Importing the numpy library and assigning it an alias of "np"

# Load in our pre-trained model and weights file
transformer = load_model('my_transformer.h5') # Loading the pre-trained model and assigning it to the variable "transformer"

# Preprocess the input article (e.g., convert it to a numerical representation)
article = preprocess(input_article) # Preprocessing the input article and assigning it to the variable "article"

# Feed the processed input through the transformer and get our output predictions
predictions = transformer.predict([np.array(article).reshape((1, 768))])[0] # Feeding the preprocessed input through the transformer and assigning the output predictions to the variable "predictions"

# Convert the output predictions to a binary classification (e.g., positive or negative)
classification = np.sign(predictions) * 2 # Converting the output predictions to a binary classification and assigning it to the variable "classification"
# Note: This line of code may not be necessary depending on the specific use case, as there are many ways to convert the output predictions to a binary classification. This is just one example.

And that’s it! Your transformer can now classify new articles without any additional updates or connections required. Pretty cool, huh?