Well, instead of being trained on just one specific task or dataset like some other models out there, OPT has been pre-trained on a massive corpus of texts from all over the internet (think Wikipedia, news articles, and social media posts). This means that when you fine-tune it for your own specific needs (like writing essays or generating product descriptions), it’s already got a pretty good idea of what language should sound like.
So how does this work in practice? Let’s say you want to write an article about the benefits of exercise, but you don’t have time to do all that research yourself. You can feed your prompt into OPT and let it generate some ideas for you based on its pre-training data. Here’s what that might look like:
# Import necessary libraries
from transformers import AutoTokenizer, TFBertForSequenceClassification
import tensorflow as tf
# Load the pre-trained model and tokenizer from Hugging Face Hub
model = TFBertForSequenceClassification.from_pretrained('opt/large')
tokenizer = AutoTokenizer.from_pretrained('opt/large')
# Define the input text (in this case, a prompt about exercise)
prompt = "What are the benefits of regular exercise?"
# Preprocess the text by tokenizing it and converting it to a list of integers that can be fed into the model.
# Tokenization is the process of breaking down a sentence into individual words or subwords, and converting them into numerical representations.
# This allows the model to understand and process the text.
input_ids = tokenizer(prompt, return_tensors='tf')['input_ids']
# Run the input through the pre-trained language model (in this case, OPT) using TensorFlow.
# The model takes in the input_ids and outputs a prediction for each class.
outputs = model(input_ids)
# Get the predicted probabilities for each class (in this case, "benefits" and "drawbacks") based on the output of the model.
# Softmax function is used to convert the model's output into probabilities.
predictions = tf.nn.softmax(outputs[0])
# Print out the top 5 predictions with their corresponding probabilities.
# The for loop iterates through the first 5 predictions and prints them out in a readable format.
for i in range(5):
print("Prediction {}: {:.4f}".format(i, predictions[0][i]))
In this example, we’re using TensorFlow to run our input through OPT and get back a list of predicted probabilities for each class (in this case, “benefits” and “drawbacks”). The output might look something like:
# This script is used to display the predicted probabilities for each class after running input through OPT using TensorFlow.
# The output is a list of probabilities for each class, with the highest probability indicating the predicted class.
# The following code displays the predicted probabilities for each class, with the class number and its corresponding probability.
# The first line of code displays the predicted probability for class 0.
Prediction 0: 0.9875
# The second line of code displays the predicted probability for class 1.
Prediction 1: 0.0024
# The third line of code displays the predicted probability for class 2.
Prediction 2: 0.0036
# The fourth line of code displays the predicted probability for class 3.
Prediction 3: 0.0012
# The fifth line of code displays the predicted probability for class 4.
Prediction 4: 0.0055
Give it a try !