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import streamlit as st
from st_pages import Page, show_pages
st.set_page_config(page_title="Question Answering", page_icon="🏠")
show_pages(
[
Page("app.py", "Home", "🏠"),
Page(
"QuestionAnswering.py", "Question Answering", "📝"
),
]
)
st.title("Project in Text Mining and Application")
st.header("Question Answering use a pre-trained model - ELECTRA")
st.markdown(
"""
**Team members:**
| Student ID | Full Name | Email |
| ---------- | ------------------------ | ------------------------------ |
| 1712603 | Lê Quang Nam | [email protected] |
| 19120582 | Lê Nhựt Minh | [email protected] |
| 19120600 | Bùi Nguyên Nghĩa | [email protected] |
| 21120198 | Nguyễn Thị Lan Anh | [email protected] |
"""
)
st.header("The Need for Question Answering")
st.markdown(
"""
...
"""
)
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 Question Answering task, we choose two related classes: ElectraTokenizerFast and
TFElectraForQuestionAnswering to implement.
"""
) |