| Name | marcel-fse JSON |
| Version |
1.7
JSON |
| download |
| home_page | None |
| Summary | Library to get sentence vectors using SIF and uSIF |
| upload_time | 2024-10-03 08:08:47 |
| maintainer | None |
| docs_url | None |
| author | Marcel Tino |
| requires_python | None |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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[English](README.md) | [Español](./docs/README.es.md) | [Français](./docs/README.fr.md) | [Deutsch](./docs/README.de.md) | [中文](./docs/README.zh.md) | [Türkçe](./docs/README.tr.md) | [日本語](./docs/README.ja.md) | [한국어](./docs/README.ko.md)
## marcel_fse
A library will help to get sentence embedding scores for user dimensions
Developed by Marcel Tino @ 2024
You can use this to alter according to your requirements.
Credits to the author for developing such an amazing code
You can find the original code here
https://github.com/oborchers/Fast_Sentence_Embeddings
Fast Sentence Embeddings
==================================
Fast Sentence Embeddings is a Python library that serves as an addition to Gensim. This library is intended to compute *sentence vectors* for large collections of sentences or documents with as little hassle as possible:
```
from marcel_fse.models.view import get_sentence_scores
import pandas as pd
import gensim.downloader as api
from nltk.tokenize import word_tokenize
df= pd.read_excel(r'C:\Users\Marcel Tino\Desktop\economy.xlsx')
df=df[['Quote']]
model_name="word2vec-google-news-300"
custom_text="economy is good"
##df will return sentence embedding scores using SIF and df1 will return sentence embedding scores using uSIF
df,df1=get_sentence_scores(df,model_name,custom_text)
```
Installation
------------
The simple way to install **marcel_fse** is:
pip install -U marcel_fse
+ Share marcel_fse on these social media platforms if you like it!
[](https://reddit.com/submit?url=https://github.com/Kanaries/pygwalker&title=Say%20Hello%20to%20pygwalker%3A%20Combining%20Jupyter%20Notebook%20with%20a%20Tableau-like%20UI)
[](https://news.ycombinator.com/submitlink?u=https://github.com/Kanaries/pygwalker)
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"description": "\r\n\r\n[English](README.md) | [Espa\u00f1ol](./docs/README.es.md) | [Fran\u00e7ais](./docs/README.fr.md) | [Deutsch](./docs/README.de.md) | [\u4e2d\u6587](./docs/README.zh.md) | [T\u00fcrk\u00e7e](./docs/README.tr.md) | [\u65e5\u672c\u8a9e](./docs/README.ja.md) | [\ud55c\uad6d\uc5b4](./docs/README.ko.md)\r\n\r\n## marcel_fse\r\n\r\nA library will help to get sentence embedding scores for user dimensions\r\n\r\nDeveloped by Marcel Tino @ 2024\r\n\r\nYou can use this to alter according to your requirements.\r\n\r\nCredits to the author for developing such an amazing code\r\n\r\nYou can find the original code here\r\n\r\nhttps://github.com/oborchers/Fast_Sentence_Embeddings\r\n\r\nFast Sentence Embeddings\r\n==================================\r\n\r\nFast Sentence Embeddings is a Python library that serves as an addition to Gensim. This library is intended to compute *sentence vectors* for large collections of sentences or documents with as little hassle as possible:\r\n\r\n```\r\n\r\nfrom marcel_fse.models.view import get_sentence_scores\r\n\r\nimport pandas as pd\r\nimport gensim.downloader as api\r\nfrom nltk.tokenize import word_tokenize\r\n\r\ndf= pd.read_excel(r'C:\\Users\\Marcel Tino\\Desktop\\economy.xlsx')\r\n\r\ndf=df[['Quote']]\r\n\r\nmodel_name=\"word2vec-google-news-300\"\r\ncustom_text=\"economy is good\"\r\n\r\n##df will return sentence embedding scores using SIF and df1 will return sentence embedding scores using uSIF\r\ndf,df1=get_sentence_scores(df,model_name,custom_text)\r\n\r\n\r\n```\r\n\r\n\r\nInstallation\r\n------------\r\nThe simple way to install **marcel_fse** is:\r\n\r\n pip install -U marcel_fse\r\n\r\n\r\n+ Share marcel_fse on these social media platforms if you like it!\r\n[](https://reddit.com/submit?url=https://github.com/Kanaries/pygwalker&title=Say%20Hello%20to%20pygwalker%3A%20Combining%20Jupyter%20Notebook%20with%20a%20Tableau-like%20UI)\r\n[](https://news.ycombinator.com/submitlink?u=https://github.com/Kanaries/pygwalker)\r\n[](https://twitter.com/share?url=https://github.com/Kanaries/pygwalker&text=Say%20Hello%20to%20pygwalker%3A%20Combining%20Jupyter%20Notebook%20with%20a%20Tableau-alternative%20UI)\r\n[](https://www.facebook.com/sharer/sharer.php?u=https://github.com/Kanaries/pygwalker)\r\n[](https://www.linkedin.com/shareArticle?url=https://github.com/Kanaries/pygwalker&&title=Say%20Hello%20to%20pygwalker%3A%20Combining%20Jupyter%20Notebook%20with%20a%20Tableau-alternative%20UI)\r\n\r\n",
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