Metadata-Version: 2.1
Name: edu-segmentation
Version: 0.0.73
Summary: 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.
Author: Your Name
Author-email: you@example.com
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
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Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
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Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Description-Content-Type: text/markdown

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: <br>
http://138.197.118.157:8000/segbot/ <br>
https://www.ijcai.org/proceedings/2018/0579.pdf

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### Authors
Code Author: Qingyi <br>
Packaging: Patria <br>

### How to Use
<li> `from edu_segmentation import run_segbot_bart`: use `run_segbot_bart.run_segbot_bart()` to perform edu-segmentation (user will be prompted for input)
