Classifier

Classifier is a powerful superclient leveraging the capabilities of LLMs and Vision LLMs to classify text and images. The classification task involves predictive modeling to assign the correct label(s) to input data.

Usage Examples

Text Classification

This superclient can classify text into predefined classes. Here’s an example:

from switchai import SwitchAI, Classifier

# Initialize the client and classifier
client = SwitchAI(provider="openai", model_name="gpt-4o")
classifier = Classifier(client, classes=["negative", "positive"])

# Classify a text
response = classifier.classify("I love this movie")
print(response)  # Output: "positive"

The response will be the predicted label for the input text.

Image Classification

Classifier can also classify images. Note that for this task, the classifier accepts only PIL images. Here’s an example:

import PIL.Image

from switchai import SwitchAI, Classifier

# Initialize the client and classifier
client = SwitchAI(provider="openai", model_name="gpt-4o")
classifier = Classifier(client, classes=["cat", "dog"])

# Open an image using PIL
pil_image = PIL.Image.open("path/to/image.jpg")

# Classify the image
response = classifier.classify(pil_image)
print(response)  # Output: "cat" or "dog"

Single-Label vs. Multi-Label Classification

By default, Classifier performs single-label classification, where each input is assigned a single label. For multi-label classification, set the multi_label parameter to True.

Here’s an example of multi-label classification, where we classify an article title into multiple categories:

from switchai import SwitchAI, Classifier

# Initialize the client and classifier
client = SwitchAI(provider="openai", model_name="gpt-4o")
classifier = Classifier(client, classes=["technology", "business", "sports"], multi_label=True)

# Classify the input text
response = classifier.classify("Apple Launches New iPhone")
print(response)  # Output: ["technology", "business"]

Task Details

In many cases, it’s useful to provide the model with additional context about the classification task. This can improve results significantly. Use the task_description parameter to specify details about the task.

For example, in a hate speech detection task, you can describe what constitutes hate speech:

from switchai import SwitchAI, Classifier

# Initialize the client and classifier
client = SwitchAI(provider="openai", model_name="gpt-4o")
classifier = Classifier(
    client,
    classes=["hate speech", "not hate speech"],
    task_description="Classify whether the input text contains "
                     "hate speech. Hate speech includes offensive language, threats, or discrimination."
)

# Classify text inputs
response = classifier.classify("Immigrants are ruining our country!")
print(response)  # Output: "hate speech"

response = classifier.classify("We need fairer policies for everyone.")
print(response)  # Output: "not hate speech"

Batch Classification

Classifier can process a list of texts or images in a single call. This feature is useful for classifying large datasets efficiently.

from switchai import SwitchAI, Classifier

# Initialize the client and classifier
client = SwitchAI(provider="openai", model_name="gpt-4o")
classifier = Classifier(client, classes=["angry", "happy", "sad", "neutral"])

# Classify multiple texts
responses = classifier.classify([
    "I love this movie",
    "I hate this movie",
    "I am feeling sad",
    "I am feeling happy"
])

print(responses)  # Output: ["happy", "angry", "sad", "happy"]

Important Notes

Classifier uses Structured Outputs, and its accuracy depends on the model and input data. Always verify the returned labels for critical use cases.