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Automating Zero-Shot Classification Generating Model Labels with GPT-3
Latest   Machine Learning

Automating Zero-Shot Classification Generating Model Labels with GPT-3

Last Updated on April 6, 2023 by Editorial Team

Author(s): Carlo Borella

Originally published on Towards AI.

Source: Image generated by the author using MidJourney

Zero-shot learning is a machine learning method that allows using a pre-trained model to classify data according to a set of classes or labels that have not been used to train the model. Zero-shot can be useful for various applications including:

  1. labeling data without spending time training or fine-tuning a model
  2. labeling data because we don’t have a training set to train or fine-tune a model

Traditionally, creating labels for a Zero-shot Classification task involves manually defining a set of potential classes or labels that the model can use to make predictions on the unseen data. This process can be time-consuming and error-prone, particularly for large datasets or complex domains.

By using OpenAI’s GPT-3 to generate labels for Zero-shot Classification models, we can significantly reduce the time and effort required to create this set of labels!

In this article, I will show how to build a zero-shot text classification model, integrating OpenAI’s GPT-3 for label creation (i.e., coming up with a set of labels relevant to the data we want to categorize) and HuggingFace zero-shot models for the actual text classification (i.e., classifying the data according to this set of labels).

To get started, we will need to install transformers and openai and import all the necessary modules:

!pip install transformers
!pip install openai

from transformers import pipeline
import torch
from tqdm import tqdm
import pandas as pd
import openai
import requests

We will also need to initialize the OpenAI API client with our API key:

openai.api_key = ("your key") #replace with your key

Now that we have the necessary packages and API key, let’s define (1) a function to generate labels relevant to the corpus of texts we want to classify and (2) a function to classify the corpus based on the set of labels previously defined.

(1) First, define a function to generate the labels we will feed to our zero-shot classification model:

def get_zero_shot_labels(domain, n_labels = None):
if n_labels is None:
prompt = 'You are picking the labels for a zero shot classification model, Generate a list of themes for the domain: {}'.format(domain)
prompt = 'You are picking the labels for a zero shot classification model, Generate a list with a minimum of {} themes for the domain: {}'.format(n_labels, domain)
response = openai.Completion.create(
text = response["choices"][0]["text"]
labels = [label.strip('-') for label in text.split('\n')]

This function uses GPT-3 (alternatively, you can use GPT-4) to generate a set of labels relevant to the specified domain we are interested in.

get_zero_shot_labels has two inputs:

  • domain is a string containing the description of the relevant domain for the data we will feed to the zero-shot classification model (e.g., restaurant reviews, football news, etc.)
  • n_labels is the minimum number of labels that we want to generate. If n_labels is not specified, the function will generate an unspecified number of labels for the specified domain.

(2) Next, we will define a function to classify our input data based on the list of possible labels we previously created with get_zero_shot_labels:

def zero_shot_classification(text_lst, labels ,model, multi_label = False):

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
classifier_pipeline = pipeline("zero-shot-classification", model = model, multi_label = multi_label, device=device)
preds = []
#loop for each text in text_list
for input_sequence in tqdm(text_lst, total=len(text_lst)):
pred = classifier_pipeline(input_sequence, labels)
label_scores = dict(zip(pred['labels'], pred['scores']))
#store the labels probabilities in a df
preds_df = pd.DataFrame(preds)


The function has 4 inputs:

  • text_lst: A list of strings representing the data to be classified.
  • labels: the set of labels to classify the data accordingly.
  • model: A string that represents the zero-shot classification model to use.
  • multi_label: A boolean flag indicating whether the model should output multiple labels per input sequence.

Once we have our label generator function and the function to classify texts, we can combine the two into an end-to-end zero-shot classifier with little human input.

So now it is time to put them together: end_to_end_zero_shot combines the label generation code with the text classifier to provide an end-to-end solution for Zero-shot text classification:

def end_to_end_zero_shot(domain, text_lst , n_labels =None , model = "valhalla/distilbart-mnli-12-3", multi_label = False):

labels = get_zero_shot_labels(domain, n_labels = n_labels)
user_input = input("proceed with these labels (yes/no)").lower()
if user_input == "yes":
preds_df = zero_shot_classification(text_lst, labels ,model, multi_label = multi_label)
print('modify your prompt and/or retry.')

I am using “valhalla/distilbart-mnli-12–3”, which is a distilled version of bart-large-mnli (it runs faster), as my default model, but you can try it with others.

In the function, I also included user_input prompting the user to examine the labels proposed by GPT before proceeding with labeling the corpus.

Finally, here is an example:

domain = 'restaurants reviews'
text_lst = [ "The food was amazing!",
"The service was slow and the staff was unfriendly.",
"The ambiance was perfect for a romantic dinner.",
"The prices were too high for the quality of the food.",
"I highly recommend this restaurant!"]

results = end_to_end_zero_shot(domain, text_lst , n_labels =None , model = "valhalla/distilbart-mnli-12-3", multi_label = False)
['Food quality', 'Service quality', 'Price', 'Atmosphere', 'Location', 'Menu', 'Dietary restrictions']
proceed with these labels (yes/no)yes
100%|██████████| 5/5 [00:09<00:00, 1.85s/it]
The predictions for each of the 5 examples we are classifying, with the probability distribution for each label. you can use the probabilities to assign a label to your text examples.

End Note

In this article, we discussed how to implement a Zero-shot text classification algorithm automating the label generation process using GPT-3 (or GPT4, even better!). For this implementation, I am assuming that the corpus we are labeling belongs to a given domain (therefore, the labels produced by GPT-3 will be relevant)

I appreciate any feedback and constructive criticism! My email is [email protected]

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