To improve EDU segmentation performance using Segbot. As Segbot has an encoder-decoder model architecture, we can replace bidirectional GRU encoder with generative pretraining models such as BART and T5. Evaluate the new model using the RST dataset by using few-shot based settings (e.g. 100 examples) to train the model, instead of using the full dataset.
Project description
Final Year Project on EDU Segmentation:
To improve EDU segmentation performance using Segbot. As Segbot has an encoder-decoder model architecture, we can replace bidirectional GRU encoder with generative pretraining models such as BART and T5. Evaluate the new model using the RST dataset by using few-shot based settings (e.g. 100 examples) to train the model, instead of using the full dataset.
Segbot:
http://138.197.118.157:8000/segbot/
https://www.ijcai.org/proceedings/2018/0579.pdf
Authors
Code Author: Qingyi
Packaging: Patria
How to Use
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for edu_segmentation-0.0.83-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 46f39faed1969cb1bdc7ca6329b19347fbd410c199af2bed4b3338762a6a1537 |
|
MD5 | 05615dc6ff7da7b931014f2421da3069 |
|
BLAKE2b-256 | 858092f02bd52c0e55f5948716986e11a0ac0ea604174d001d42ff70d172027a |