A collection of parsers for LLM benchmark datasets
Convert PDFs to a dataset and upload to Hugging Face
Rename models in dataset leaderboard
Generate a Parquet file for dataset validation
Access NLPre-PL dataset and pre-trained models
ReWrite datasets with a text instruction
Evaluate evaluators in Grounded Question Answering
Create datasets with FAQs and SFT prompts
Browse and search datasets
Annotation Tool
Organize and process datasets efficiently
Browse and view Hugging Face datasets
Convert and PR models to Safetensors
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.
pip install llm-eval-parser to install the tool.from llm_eval_parser import DatasetParser in your script.dataset.json).parse() method to convert the dataset into a standardized format.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.