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| import streamlit as st | |
| from utilities import initialization | |
| st.set_page_config(page_title="Top2Vec", layout="wide") | |
| initialization() | |
| vb_link = 'https://visitor-badge.glitch.me/badge?page_id=demo-org.Top2Vec&left_color=gray&right_color=blue' | |
| visitor_badge = f"" | |
| st.markdown( | |
| f""" | |
| # Introduction | |
| This is [space](https://huggingface.co/spaces) dedicated to using [top2vec](https://github.com/ddangelov/Top2Vec) and showing what features are available for semantic searching and topic modeling. | |
| Please check out this [readme](https://github.com/ddangelov/Top2Vec#how-does-it-work) to better understand how it works. | |
| > Top2Vec is an algorithm for **topic modeling** and **semantic search**. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors. | |
| # Setup | |
| I used the [20 NewsGroups](https://huggingface.co/datasets/SetFit/20_newsgroups) dataset with `top2vec`. | |
| I fit on the dataset and reduced the topics to 20. | |
| The topics are created from top2vec, not the labels. | |
| No analysis on the top 20 topics vs labels is provided. | |
| # Usage | |
| Check out | |
| - The [Topic Explorer](/Topic_Explorer) page to understand what topic were detected | |
| - The [Document Explorer](/Document_Explorer) page to visually explore documents | |
| - The [Semantic Search](/Semantic_Search) page to search by meaning | |
| {visitor_badge} | |
| """ | |
| ) | |