LLMEval Dataset Parser

What is LLMEval Dataset Parser ?

LLMEval Dataset Parser is a tool designed to streamline the process of working with large language model (LLM) benchmark datasets. It provides a unified interface for parsing and organizing datasets, making it easier to analyze and compare the performance of different LLMs. The tool supports a variety of dataset formats and simplifies the extraction of relevant information for benchmarking purposes.

Features

• Support for multiple dataset formats: Handles JSON, CSV, and text files out of the box.
• Standardized output: Converts datasets into a consistent format for easier comparison and analysis.
• Customizable parsing: Allows users to define specific parsing rules based on their needs.
• Integration with popular libraries: Works seamlessly with libraries like Hugging Face's datasets.
• Efficient processing: Optimized for handling large-scale datasets.

How to use LLMEval Dataset Parser ?

1. Install the package: Run pip install llm-eval-parser to install the tool.
2. Import the parser: Use from llm_eval_parser import DatasetParser in your script.
3. Load a dataset: Specify the path to your dataset file (e.g., dataset.json).
4. Parse the dataset: Call the parse() method to convert the dataset into a standardized format.

Frequently Asked Questions

1. What file formats does LLMEval Dataset Parser support?
LLMEval Dataset Parser supports JSON, CSV, and plain text files. Additional formats can be added through custom parsers.

2. Can I customize the parsing process?
Yes, users can define custom parsing rules by creating configuration files that specify how to process each dataset.

3. Is LLMEval Dataset Parser suitable for large datasets?
Yes, the tool is optimized for handling large-scale datasets. However, very large files may require additional memory or processing power.