ollama-classifier 0.2.0

A wrapper around Ollama Python SDK for text classification with constrained output and confidence scoring

ollama-classifier

A Python wrapper around the Ollama Python SDK for text classification with constrained output and confidence scoring.

Features

• Constrained Output: Uses JSON schema with enum constraints to ensure only valid choices are generated
• Confidence Scoring: Multi-call evaluation with softmax for calibrated probabilities
• Sync & Async: Full support for both synchronous and asynchronous operations
• Batch Processing: Classify multiple texts efficiently
• Flexible Choices: Support for simple labels or labels with descriptions
• Custom Prompts: Override the default system prompt for specialized tasks

Installation

pip install ollama-classifier

Or with uv:

uv add ollama-classifier

Prerequisites

• Ollama installed and running
• A model pulled (e.g., ollama pull llama3.2)

Quick Start

from ollama import Client
from ollama_classifier import OllamaClassifier

client = Client()
classifier = OllamaClassifier(client, model="llama3.2")

result = classifier.classify(
    text="I love this product!",
    choices=["positive", "negative", "neutral"]
)

print(f"Prediction: {result.prediction}")
print(f"Confidence: {result.confidence:.2%}")
print(f"Probabilities: {result.probabilities}")

Usage

Basic Classification

from ollama import Client
from ollama_classifier import OllamaClassifier

client = Client()
classifier = OllamaClassifier(client, model="llama3.2")

result = classifier.classify(
    text="The goalkeeper made an incredible save!",
    choices=["sports", "politics", "technology", "entertainment"]
)

Classification with Label Descriptions

Providing descriptions helps the model understand each category better:

choices = {
    "positive": "Text expresses happiness, satisfaction, or approval",
    "negative": "Text expresses anger, disappointment, or disapproval",
    "mixed": "Text contains both positive and negative sentiments",
    "neutral": "Text is factual without strong emotional content",
}

result = classifier.classify(
    text="The food was amazing but the service was terrible.",
    choices=choices
)

Custom System Prompt

result = classifier.classify(
    text="The quarterly earnings exceeded analyst expectations.",
    choices=["bullish", "bearish", "neutral"],
    system_prompt="You are a financial sentiment analyzer. "
                  "Classify financial news based on market sentiment."
)

Scoring (Multi-Call with Softmax)

Get calibrated probability distribution over all choices. Makes N API calls for N choices:

result = classifier.score(
    text="The movie was fantastic!",
    choices=["positive", "negative", "neutral"]
)

Generate Only (Fastest)

When you only need the prediction without confidence scores:

prediction = classifier.generate(
    text="The team won the championship!",
    choices=["sports", "finance", "politics"]
)

Batch Classification

texts = [
    "The goalkeeper made an incredible save!",
    "The central bank raised interest rates.",
    "The new smartphone features a revolutionary camera.",
]

results = classifier.batch_classify(
    texts=texts,
    choices=["sports", "finance", "technology"]
)

for text, result in zip(texts, results):
    print(f"{text} -> {result.prediction} ({result.confidence:.2%})")

Async Usage

import asyncio
from ollama import AsyncClient
from ollama_classifier import OllamaClassifier

async def main():
    client = AsyncClient()
    classifier = OllamaClassifier(client, model="llama3.2")
    
    # Single classification
    result = await classifier.aclassify(
        text="The concert was amazing!",
        choices=["positive", "negative", "neutral"]
    )
    
    # Batch classification (concurrent)
    results = await classifier.abatch_classify(
        texts=["Text 1", "Text 2", "Text 3"],
        choices=["positive", "negative", "neutral"]
    )

asyncio.run(main())

API Reference

ClassificationResult

@dataclass
class ClassificationResult:
    prediction: str              # The predicted choice label
    confidence: float            # Confidence score (0.0 to 1.0)
    probabilities: Dict[str, float]  # Probability distribution over all choices
    raw_response: Dict           # Raw Ollama response for debugging

OllamaClassifier Methods

Method	Async	Description
generate(text, choices, system_prompt)	agenerate	Constrained output only (fastest)
score(text, choices, system_prompt)	ascore	Multi-call evaluation with softmax
classify(text, choices, system_prompt)	aclassify	Full classification with confidence scores
batch_generate(texts, choices, system_prompt)	abatch_generate	Batch constrained output
batch_score(texts, choices, system_prompt)	abatch_score	Batch scoring
batch_classify(texts, choices, system_prompt)	abatch_classify	Batch classification

Parameters

• text (str): The text to classify
• texts (List[str]): List of texts to classify (batch methods)
• choices (Union[List[str], Dict[str, str]]): Either a list of choice labels, or a dict mapping labels to descriptions
• system_prompt (str | None): Optional custom system prompt

Choosing a Method

Use Case	Recommended Method
Speed is critical, no confidence needed	generate
Accurate confidence scores	classify / score
Batch processing	batch_classify or batch_score
Concurrent processing	Async variants (aclassify, etc.)

License

MIT License

Development

This project just started! Looking forward to suggestions, issues, and pull requests!