edu-segmentation


Nameedu-segmentation JSON
Version 0.0.87 PyPI version JSON
download
home_page
SummaryTo 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.
upload_time2023-05-26 11:40:18
maintainer
docs_urlNone
authorYour Name
requires_python
license
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            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

----
### 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)

            

Raw data

            {
    "_id": null,
    "home_page": "",
    "name": "edu-segmentation",
    "maintainer": "",
    "docs_url": null,
    "requires_python": "",
    "maintainer_email": "",
    "keywords": "",
    "author": "Your Name",
    "author_email": "you@example.com",
    "download_url": "https://files.pythonhosted.org/packages/e2/16/677a0a8d9ca1282597d53313082116d1eb8f41566e9771a706667e138da4/edu_segmentation-0.0.87.tar.gz",
    "platform": null,
    "description": "Final Year Project on EDU Segmentation:\n\nTo 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.\n\nSegbot: <br>\nhttp://138.197.118.157:8000/segbot/ <br>\nhttps://www.ijcai.org/proceedings/2018/0579.pdf\n\n----\n### Authors\nCode Author: Qingyi <br>\nPackaging: Patria <br>\n\n### How to Use\n<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)\n",
    "bugtrack_url": null,
    "license": "",
    "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.",
    "version": "0.0.87",
    "project_urls": null,
    "split_keywords": [],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "aa40e3f484bd3bf4972772b3f681df81f419c43b1c8351ca5f89bcfc51e1f2e6",
                "md5": "dc13edfa64967fc05a408b1c37347627",
                "sha256": "5eb9f74490e31dbb901d6075315b251c4535554c157108ad30aac64769606f6d"
            },
            "downloads": -1,
            "filename": "edu_segmentation-0.0.87-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "dc13edfa64967fc05a408b1c37347627",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": null,
            "size": 327547,
            "upload_time": "2023-05-26T11:40:15",
            "upload_time_iso_8601": "2023-05-26T11:40:15.483299Z",
            "url": "https://files.pythonhosted.org/packages/aa/40/e3f484bd3bf4972772b3f681df81f419c43b1c8351ca5f89bcfc51e1f2e6/edu_segmentation-0.0.87-py2.py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "e216677a0a8d9ca1282597d53313082116d1eb8f41566e9771a706667e138da4",
                "md5": "759cf637e9ea36c6c096d4fd75e5b71d",
                "sha256": "2388e5c69c20ff08911c9c5fd2537ec1f7dcb5f3f25dfad08d709e74b441a2c1"
            },
            "downloads": -1,
            "filename": "edu_segmentation-0.0.87.tar.gz",
            "has_sig": false,
            "md5_digest": "759cf637e9ea36c6c096d4fd75e5b71d",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 334801,
            "upload_time": "2023-05-26T11:40:18",
            "upload_time_iso_8601": "2023-05-26T11:40:18.611491Z",
            "url": "https://files.pythonhosted.org/packages/e2/16/677a0a8d9ca1282597d53313082116d1eb8f41566e9771a706667e138da4/edu_segmentation-0.0.87.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-05-26 11:40:18",
    "github": false,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "lcname": "edu-segmentation"
}
        
Elapsed time: 0.11937s