A collection of parsers for LLM benchmark datasets
Convert PDFs to a dataset and upload to Hugging Face
Transfer datasets from HuggingFace to ModelScope
Create a domain-specific dataset project
Display translation benchmark results from NTREX dataset
Search narrators and view network connections
Display trending datasets from Hugging Face
Search for Hugging Face Hub models
Clean and process datasets
Organize and process datasets for AI models
List of French datasets not referenced on the Hub
Access NLPre-PL dataset and pre-trained models
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.