alon-albalak/TLiDB
Transfer Learning in Dialogue Benchmarking Toolkit
This toolkit helps conversational AI researchers and practitioners evaluate and compare different methods for transferring learned knowledge across various dialogue tasks. It takes in conversational datasets and model configurations, then outputs standardized performance metrics, helping you understand how well models adapt to new, limited data scenarios in areas like customer service bots or virtual assistants. It's designed for machine learning researchers and NLP engineers working on advanced dialogue systems.
No commits in the last 6 months. Available on PyPI.
Use this if you are a researcher or engineer looking to benchmark transfer learning approaches for conversational AI and need a standardized way to evaluate model performance across different dialogue tasks and datasets.
Not ideal if you are a business user simply looking for a ready-to-deploy conversational AI solution without needing to perform deep research or model benchmarking.
Stars
14
Forks
5
Language
Python
License
MIT
Category
Last pushed
Mar 31, 2023
Commits (30d)
0
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