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Project description
AutoEvaluator: An LLM based LLM Evaluator
AutoEvaluator is a Python library that speeds up the large language models (LLMs) output generation QC work. It provides a simple, transparent, and user-friendly API to identify the True Positives (TP), False Positives (FP), and False Negatives (FN) statements based the generated statement and ground truth provided. Get ready to turbocharge your LLM evaluations!
Features:
- Evaluate LLM outputs against a reference dataset or human judgement.
- Generate TP, FP, and FN sentences based on ground truth provided
Installation
Autoevaluator requires Python 3.9
and several dependencies. You can install autoevaluator:
pip install autoevaluator
Usage
-
Prepare your data:
- Create a dataset containing LLM outputs and their corresponding ground truth labels.
- The format of the data can be customized depending on the evaluation task.
- Example: A CSV file with columns for "prompt," "llm_output," and "ground_truth"
-
setup environment variables
import os
os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"
os.environ["AZURE_OPENAI_API_KEY"] = "<AZURE_OPENAI_API_KEY>"
os.environ["AZURE_OPENAI_ENDPOINT"] = "<AZURE_OPENAI_ENDPOINT>"
os.environ["DEPLOYMENT"] = "<azure>/<not-azure>"
- run autoevaluator
from autoevaluator import evaluate
eval_results = evaluate(generated_statement, ground_truth)
- Output:
- The script will generate a dictionary with the following information:
- TP, FP, and FN sentences
- The script will generate a dictionary with the following information:
License:
This project is licensed under the MIT License. See the LICENSE
file for details.
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