Abstract
Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flambé achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.- Anthology ID:
- P19-3029
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Marta R. Costa-jussà, Enrique Alfonseca
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 181–188
- Language:
- URL:
- https://aclanthology.org/P19-3029
- DOI:
- 10.18653/v1/P19-3029
- Cite (ACL):
- Jeremy Wohlwend, Nicholas Matthews, and Ivan Itzcovich. 2019. Flambé: A Customizable Framework for Machine Learning Experiments. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 181–188, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Flambé: A Customizable Framework for Machine Learning Experiments (Wohlwend et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P19-3029.pdf
- Data
- SST, SST-2