Knowledge-data-evaluation

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

Knowledge-data-evaluation

(★ 42)

No description available.

Knowledge-data-evaluation Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Knowledge-data-evaluation
A high-efficiency data management tool designed for analyzing entity relationships and comprehensively evaluating data quality.
## Overview
This system leverages advanced algorithms to evaluate data quality across multiple dimensions, including quantity, consistency, and association, providing a clear view of data quality to assist users in optimizing datasets.
## Installation Guide
1. Ensure Python 3.8 or higher is installed.
2. Install ber-base-chinese https://huggingface.co/google-bert/bert-base-chinese
3. Clone the repository and install dependencies: git clone https://github.com/Learning0411/Knowledge-data-evaluation.git
## Configuration
Before running the system, you may need to configure the path to your own `bert-base-chinese` model in `similarity_computation.py`. Replace the default path with the path to your local model directory.
## Usage
To run the system, you need to prepare your data as triples in a CSV file. Each row in the CSV should represent a triple with three columns: subject, predicate, and object.
## Contribution Guidelines
Contributions and pull requests are welcome. Please adhere to the guidelines specified in the CONTRIBUTING.md file.

## Maintainers
Principal Developer: Learning0411 gww723 lilinze123 chanjuanzhou wkq8008 same0709 haha123agfd Liusf6416 hankatsufumi yiayg wclftx crimsondde cquptljl
2048kbs

Knowledge-data-evaluation

Knowledge-data-evaluation Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Knowledge-data-evaluation

Knowledge-data-evaluation Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README