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A Pydantic data model class that defines the configuration schema for a text summarization model, including model name, token limits, and temperature settings.

Purpose

This class serves as a data validation and serialization model for summarization model configurations. It leverages Pydantic's BaseModel to provide automatic validation, type checking, and serialization/deserialization capabilities for summarization model parameters. It's typically used to configure and validate settings before passing them to an actual summarization API or model.

Source Code

class SummarizationModel(BaseModel):
    model_name: str
    max_tokens: int
    temperature: float

Parameters

Parameter Details

model_name: String identifier for the summarization model to be used (e.g., 'gpt-3.5-turbo', 'claude-2'). This specifies which AI model will perform the summarization task.

max_tokens: Integer value representing the maximum number of tokens the model should generate in the summary. Controls the length of the output summary.

temperature: Float value (typically between 0.0 and 2.0) that controls the randomness of the model's output. Lower values (closer to 0) make output more deterministic and focused, while higher values increase creativity and randomness.

Return Value

Instantiation returns a SummarizationModel object with validated attributes. The class itself doesn't have explicit return methods beyond Pydantic's built-in methods like dict(), json(), which return dictionary and JSON string representations respectively.

Class Interface

Methods

Attributes

Dependencies

• pydantic

Required Imports

from pydantic import BaseModel

Usage Example

from pydantic import BaseModel

class SummarizationModel(BaseModel):
    model_name: str
    max_tokens: int
    temperature: float

# Instantiate with valid parameters
config = SummarizationModel(
    model_name="gpt-3.5-turbo",
    max_tokens=150,
    temperature=0.7
)

# Access attributes
print(config.model_name)  # Output: gpt-3.5-turbo
print(config.max_tokens)  # Output: 150

# Convert to dictionary
config_dict = config.dict()
print(config_dict)  # Output: {'model_name': 'gpt-3.5-turbo', 'max_tokens': 150, 'temperature': 0.7}

# Convert to JSON
config_json = config.json()
print(config_json)  # Output: JSON string

# Create from dictionary
config2 = SummarizationModel(**{'model_name': 'claude-2', 'max_tokens': 200, 'temperature': 0.5})

Best Practices

• Always provide all required fields (model_name, max_tokens, temperature) when instantiating the class to avoid Pydantic validation errors.
• Use appropriate temperature values (typically 0.0-2.0) based on your use case: lower for factual summaries, higher for creative ones.
• Set max_tokens based on your desired summary length and the model's capabilities.
• Leverage Pydantic's validation by catching ValidationError exceptions when creating instances with invalid data.
• Use the .dict() method to convert the model to a dictionary for passing to API calls.
• Consider adding Field validators from Pydantic if you need to enforce constraints (e.g., temperature range 0.0-2.0).
• This class is immutable by default in Pydantic v2; use model_copy() to create modified versions.
• The class is stateless and thread-safe, making it suitable for concurrent usage.

Tags

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