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Rename app_Homepage.py to app.py
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import streamlit as st
from st_pages import Page, show_pages
st.set_page_config(page_title="Information Retrieval", page_icon="🏠")
show_pages(
[
Page("app.py", "Home", "🏠"),
Page(
"Information_Retrieval.py", "Information Retrieval", "📝"
),
]
)
st.title("Project in Text Mining and Application - Information Retrieval")
st.markdown(
"""
**Team members:**
| Student ID | Full Name | Email |
| ---------- | ------------------------ | ------------------------------ |
| 1712603 | Lê Quang Nam | 1712603@student.hcmus.edu.vn |
| 19120582 | Lê Nhựt Minh | 19120582@student.hcmus.edu.vn |
| 19120600 | Bùi Nguyên Nghĩa | 19120600@student.hcmus.edu.vn |
| 21120198 | Nguyễn Thị Lan Anh | 21120198@student.hcmus.edu.vn |
"""
)
st.header("The Need for Information Retrieval")
st.markdown(
"""
The task of classifying whether a question and a context paragraph are related to
each other is based on two main steps: word embedding and classifier. Both of these
steps together constitute the process of analyzing and evaluating the relationship
between the question and the context.
"""
)
st.header("Technology used")
st.markdown(
"""
The ELECTRA model, specifically the "google/electra-small-discriminator" used here,
is a deep learning model in the field of natural language processing (NLP) developed
by Google. This model is an intelligent variation of the supervised learning model
based on the Transformer architecture, designed to understand and process natural language efficiently.
For this text classification task, we choose two related classes: ElectraTokenizer and
FElectraForSequenceClassification to implement.
"""
)