Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/stanford-futuredata/ARES
https://github.com/stanford-futuredata/ARES
Last synced: about 1 month ago
JSON representation
- Host: GitHub
- URL: https://github.com/stanford-futuredata/ARES
- Owner: stanford-futuredata
- License: apache-2.0
- Created: 2023-09-27T03:56:19.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-21T19:36:23.000Z (7 months ago)
- Last Synced: 2024-05-22T08:00:46.469Z (7 months ago)
- Language: Python
- Homepage: https://ares-ai.vercel.app/
- Size: 252 MB
- Stars: 312
- Watchers: 10
- Forks: 32
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-LLMs-Datasets - https://github.com/stanford-futuredata/ARES
- awesome-llm-eval - ARES - 文档-答案三元组。ARES培训流程包括三个步骤:(1)从领域内段落生成合成查询和答案。(2)通过在合成生成的训练数据上进行微调,为评分RAG系统准备LLM评委。(3)部署准备好的LLM评委以评估您的RAG系统在关键性能指标上的表现 (2023-09-27) | (Datasets-or-Benchmark / RAG检索增强生成评估)
- awesome-production-machine-learning - ARES - futuredata/ARES.svg?style=social) - ARES is a framework for automatically evaluating Retrieval-Augmented Generation (RAG) models. (Evaluation and Monitoring)
- StarryDivineSky - stanford-futuredata/ARES